IDEAS home Printed from https://ideas.repec.org/f/c/pme404.html

Giovanni Mellace

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Riccardo Di Francesco & Giovanni Mellace, 2025. "Causal Inference for Qualitative Outcomes," Papers 2502.11691, arXiv.org, revised Jun 2025.

    Cited by:

    1. Young Ahn & Hiroyuki Kasahara, 2026. "Event-Study Designs for Discrete Outcomes under Transition Independence," Papers 2603.07914, arXiv.org.
    2. Sokolov, Boris, 2025. "Causal Estimands for Policy Evaluation and Beyond," SocArXiv 4vtpk_v1, Center for Open Science.

  2. Roberta Di Stefano & Giovanni Mellace, 2024. "The inclusive Synthetic Control Method," Papers 2403.17624, arXiv.org, revised Nov 2024.

    Cited by:

    1. Tuomas Kaariaho, 2025. "Unintended Effects of Germany’s Nuclear Phase Out: A Rise in Mortality Due to Non-Communicable Respiratory Diseases," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 88(8), pages 2085-2123, August.
    2. Dan S. Rickman & Hongbo Wang, 2023. "Creating and maintaining film clusters: Synthetic control method analysis of the enactment and repeal of US state film incentives," Papers in Regional Science, Wiley Blackwell, vol. 102(2), pages 363-392, April.
    3. Renan Serenini & Frantisek Masek, 2025. "Spatial Synthetic Difference-in-Differences," Working and Discussion Papers WP 11/2025, Research Department, National Bank of Slovakia.
    4. Mario Tello-Pacheco, 2023. "Los “spillovers” del COVID-19 sobre el empleo y el ingreso en Perú," Apuntes del Cenes, Universidad Pedagógica y Tecnológica de Colombia, vol. 42(75), pages 161-195.
    5. Harry Aytug, 2026. "To Adopt or Not to Adopt: Heterogeneous Trade Effects of the Euro," Papers 2601.19664, arXiv.org, revised Jan 2026.
    6. David Gilchrist & Thomas Emery & Nuno Garoupa & Rok Spruk, 2023. "Synthetic Control Method: A tool for comparative case studies in economic history," Journal of Economic Surveys, Wiley Blackwell, vol. 37(2), pages 409-445, April.
    7. Aleksandar Keseljevic & Rok Spruk, 2022. "Estimating the Effects of Syrian Civil War," Papers 2209.03046, arXiv.org.
    8. Dan S. Rickman & Hongbo Wang, 2024. "Estimating the economic effects of US state and local fiscal policy: A synthetic control method matching‐regression approach," Growth and Change, Wiley Blackwell, vol. 55(2), June.
    9. Giulio Grossi & Marco Mariani & Alessandra Mattei & Patrizia Lattarulo & Ozge Oner, 2020. "Direct and spillover effects of a new tramway line on the commercial vitality of peripheral streets. A synthetic-control approach," Papers 2004.05027, arXiv.org, revised Nov 2023.

  3. Nadja van ’t Hoff & Arthur Lewbel & Giovanni Mellace, 2023. "Limited Monotonicity and the Combined Compliers LATE," Boston College Working Papers in Economics 1059, Boston College Department of Economics, revised 20 Jan 2025.

    Cited by:

    1. Goff, Leonard, 2024. "A vector monotonicity assumption for multiple instruments," Journal of Econometrics, Elsevier, vol. 241(1).
    2. Luther Yap, 2025. "Sensitivity of Policy‐Relevant Treatment Parameters to Violations of Monotonicity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 40(6), pages 627-638, September.
    3. Di Francesco, Riccardo & Mellace, Giovanni, 2025. "Causal inference for qualitative outcomes," Economics Letters, Elsevier, vol. 256(C).
    4. Yuta Ota & Takahiro Hoshino & Taisuke Otsu, 2025. "Causal Inference With Auxiliary Observations," Keio-IES Discussion Paper Series 2025-021, Institute for Economics Studies, Keio University.
    5. Ricardo E. Miranda, 2026. "On the falsification of instrumental variable models for heterogeneous treatment effects," Papers 2601.14464, arXiv.org.
    6. Leonard Goff, 2024. "When does IV identification not restrict outcomes?," Papers 2406.02835, arXiv.org, revised Feb 2025.

  4. Crudu, Federico & Mellace, Giovanni & Smits, Joeri & Knaus, Michael, 2022. "What does OLS identify under the zero conditional mean assumption?," Discussion Papers on Economics 3/2022, University of Southern Denmark, Department of Economics, revised 15 Nov 2022.

    Cited by:

    1. Goel, Deepti, 2025. "Estimator of What? A Note on Teaching Regressions in Introductory Econometrics," GLO Discussion Paper Series 1646, Global Labor Organization (GLO).

  5. Federico Crudu & Michael C. Knaus & Giovanni Mellace & Joeri Smits, 2022. "On the Role of the Zero Conditional Mean Assumption for Causal Inference in Linear Models," Papers 2211.09502, arXiv.org.

    Cited by:

    1. Bonev, Petyo, 2025. "Behavioral spillovers," Journal of Economic Behavior & Organization, Elsevier, vol. 229(C).
    2. Bonev, Petyo, 2023. "Behavioral Spillovers," Economics Working Paper Series 2303, University of St. Gallen, School of Economics and Political Science.

  6. Lafférs, Lukáš & Mellace, Giovanni, 2020. "Identification of the average treatment effect when SUTVA is violated," Discussion Papers on Economics 3/2020, University of Southern Denmark, Department of Economics.

    Cited by:

    1. Arthur Lewbel, 2018. "The Identification Zoo - Meanings of Identification in Econometrics," Boston College Working Papers in Economics 957, Boston College Department of Economics, revised 14 Dec 2019.
    2. Lafférs, Lukáš & Nedela, Roman, 2025. "Sensitivity of Bounds on ATEs under Survey Nonresponse," Econometrics and Statistics, Elsevier, vol. 34(C), pages 1-13.

  7. Mellace, Giovanni & Pasquini, Alessandra, 2019. "Identify More, Observe Less: Mediation Analysis: Mediation Analysis Synthetic Control," Discussion Papers on Economics 12/2019, University of Southern Denmark, Department of Economics.

    Cited by:

    1. Roberta Di Stefano & Giovanni Mellace, 2020. "The inclusive synthetic control method," Working Papers 21/20, Sapienza University of Rome, DISS.

  8. Giovanni Mellace & Alessandra Pasquini, 2019. "Identify More, Observe Less: Mediation Analysis Synthetic Control," CEIS Research Paper 474, Tor Vergata University, CEIS, revised 20 Nov 2019.

    Cited by:

    1. Viviana Celli, 2022. "Causal mediation analysis in economics: Objectives, assumptions, models," Journal of Economic Surveys, Wiley Blackwell, vol. 36(1), pages 214-234, February.
    2. Roberta Di Stefano & Giovanni Mellace, 2020. "The inclusive synthetic control method," Working Papers 21/20, Sapienza University of Rome, DISS.

  9. Federico Crudu & Giovanni Mellace & Zsolt Sandor, 2017. "Inference in instrumental variables models with heteroskedasticity and many instruments," Department of Economics University of Siena 761, Department of Economics, University of Siena.

    Cited by:

    1. Dennis Lim & Wenjie Wang & Yichong Zhang, 2022. "A Conditional Linear Combination Test with Many Weak Instruments," Papers 2207.11137, arXiv.org, revised Apr 2023.
    2. Anna Baiardi & Paul S. Clarke & Andrea A. Naghi & Annalivia Polselli, 2026. "Double Machine Learning for Static Panel Data with Instrumental Variables: New Method and Applications," Papers 2603.20464, arXiv.org.
    3. Johannes W. Ligtenberg & Tiemen Woutersen, 2024. "Multidimensional clustering in judge designs," Papers 2406.09473, arXiv.org.
    4. Doko Tchatoka, Firmin & Wang, Wenjie, 2025. "Identification-Robust Two-Stage Bootstrap Tests with Pretesting for Exogeneity," MPRA Paper 125017, University Library of Munich, Germany.
    5. Eleonora Brandimarti, 2025. "Self-Selection, University Courses and Returns to Advanced Degrees," Papers 2511.09260, arXiv.org.
    6. Anatolyev, Stanislav & Sølvsten, Mikkel, 2023. "Testing many restrictions under heteroskedasticity," Journal of Econometrics, Elsevier, vol. 236(1).
    7. Hongwei Shi & Xinyu Zhang & Xu Guo & Baihua He & Chenyang Wang, 2025. "Testing overidentifying restrictions on high-dimensional instruments and covariates," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 77(2), pages 331-352, April.
    8. Manu Navjeevan, 2023. "An Identification and Dimensionality Robust Test for Instrumental Variables Models," Papers 2311.14892, arXiv.org, revised Dec 2024.
    9. Tom Boot & Johannes W. Ligtenberg, 2023. "Identification- and many moment-robust inference via invariant moment conditions," Papers 2303.07822, arXiv.org, revised Oct 2025.
    10. Qu Feng & Sombut Jaidee & Wenjie Wang, 2025. "Robust Inference with High-Dimensional Instruments," Papers 2506.23834, arXiv.org.
    11. Johannes W. Ligtenberg, 2023. "Inference in clustered IV models with many and weak instruments," Papers 2306.08559, arXiv.org, revised Oct 2025.
    12. Federico Crudu & Giovanni Mellace & Zsolt S'andor, 2026. "Jackknife Instrumental Variable Inference," Papers 2604.15437, arXiv.org.
    13. Tom Boot & Didier Nibbering, 2024. "Inference on LATEs with covariates," Papers 2402.12607, arXiv.org, revised Dec 2024.
    14. Boot, Tom & Ligtenberg, Johannes W., 2025. "Identification- and many moment-robust inference via invariant moment conditions," Journal of Econometrics, Elsevier, vol. 252(PA).
    15. Matsushita, Yukitoshi & Otsu, Taisuke, 2024. "A jackknife Lagrange multiplier test with many weak instruments," LSE Research Online Documents on Economics 116392, London School of Economics and Political Science, LSE Library.
    16. Max-Sebastian Dov`i, 2021. "Inference on the New Keynesian Phillips Curve with Very Many Instrumental Variables," Papers 2101.09543, arXiv.org, revised Mar 2021.
    17. Max-Sebastian Dov`i & Anders Bredahl Kock & Sophocles Mavroeidis, 2022. "A Ridge-Regularised Jackknifed Anderson-Rubin Test," Papers 2209.03259, arXiv.org, revised Nov 2023.
    18. Dennis Lim & Wenjie Wang & Yichong Zhang, 2024. "A Dimension-Agnostic Bootstrap Anderson-Rubin Test For Instrumental Variable Regressions," Papers 2412.01603, arXiv.org, revised Sep 2025.
    19. Chao, John C. & Swanson, Norman R. & Woutersen, Tiemen, 2023. "Jackknife estimation of a cluster-sample IV regression model with many weak instruments," Journal of Econometrics, Elsevier, vol. 235(2), pages 1747-1769.
    20. Lim, Dennis & Wang, Wenjie & Zhang, Yichong, 2024. "A conditional linear combination test with many weak instruments," Journal of Econometrics, Elsevier, vol. 238(2).
    21. Luther Yap, 2024. "Inference with Many Weak Instruments and Heterogeneity," Papers 2408.11193, arXiv.org, revised Apr 2025.
    22. Wenze Li, 2025. "An Empirical Comparison of Weak-IV-Robust Procedures in Just-Identified Models," Papers 2506.18001, arXiv.org.
    23. Christis Katsouris, 2023. "Optimal Estimation Methodologies for Panel Data Regression Models," Papers 2311.03471, arXiv.org, revised Nov 2023.
    24. Anna Mikusheva & Liyang Sun, 2023. "Weak Identification with Many Instruments," Papers 2308.09535, arXiv.org, revised Jan 2024.

  10. Dahl, Christian M. & Huber, Martin & Mellace, Giovanni, 2017. "It's never too LATE: A new look at local average treatment effects with or without defiers," Discussion Papers on Economics 2/2017, University of Southern Denmark, Department of Economics.

    Cited by:

    1. Black, Dan A. & Joo, Joonhwi & LaLonde, Robert & Smith, Jeffrey A. & Taylor, Evan J., 2022. "Simple Tests for Selection: Learning More from Instrumental Variables," Labour Economics, Elsevier, vol. 79(C).
    2. Danny Cohen-Zada & Itay Attar & Todd Elder, 2024. "Measuring and Correcting Monotonicity Bias:The Case of School Entrance Age Effects," Working Papers 2406, Ben-Gurion University of the Negev, Department of Economics.
    3. Mario Fiorini & Katrien Stevens, 2021. "Scrutinizing the Monotonicity Assumption in IV and fuzzy RD designs," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(6), pages 1475-1526, December.
    4. Johann Caro-Burnett, 2026. "Toggling the Defiers to Relax Monotonicity: The Difference-in-Instrumental-Variables Estimand," Papers 2602.12504, arXiv.org.
    5. Luther Yap, 2025. "Sensitivity of Policy‐Relevant Treatment Parameters to Violations of Monotonicity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 40(6), pages 627-638, September.
    6. Yuta Ota & Takahiro Hoshino & Taisuke Otsu, 2025. "Causal Inference With Auxiliary Observations," Keio-IES Discussion Paper Series 2025-021, Institute for Economics Studies, Keio University.
    7. Ricardo E. Miranda, 2026. "On the falsification of instrumental variable models for heterogeneous treatment effects," Papers 2601.14464, arXiv.org.
    8. Seltzer, Andrew J. & Wadsworth, Jonathan, 2023. "The impact of public transportation and commuting on urban labor markets: evidence from the New Survey of London Life and Labour, 1929–1932," LSE Research Online Documents on Economics 120895, London School of Economics and Political Science, LSE Library.
    9. Nadja van ’t Hoff & Arthur Lewbel & Giovanni Mellace, 2023. "Limited Monotonicity and the Combined Compliers LATE," Boston College Working Papers in Economics 1059, Boston College Department of Economics, revised 20 Jan 2025.
    10. Sun, Zhenting & Wüthrich, Kaspar, 2025. "Pairwise valid instruments," Journal of Econometrics, Elsevier, vol. 250(C).
    11. Zhenting Sun & Kaspar Wuthrich, 2022. "Pairwise Valid Instruments," Papers 2203.08050, arXiv.org, revised Feb 2025.
    12. Camilo Garcia-Jimeno & Sahar Parsa, 2024. "Cultural Change Through Writing Style: Gendered Pronoun Use in the Economics Profession," Working Paper Series WP 2024-23, Federal Reserve Bank of Chicago.
    13. Huntington-Klein Nick, 2020. "Instruments with Heterogeneous Effects: Bias, Monotonicity, and Localness," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 182-208, January.
    14. Claudia Noack, 2021. "Sensitivity of LATE Estimates to Violations of the Monotonicity Assumption," Papers 2106.06421, arXiv.org.

  11. Kongstad, L.P. & Mellace, G. & Olsen, K.R., 2016. "Can the use of Electronic Health Records in General Practice reduce hospitalizations for diabetes patients? Evidence from a natural experiment," Health, Econometrics and Data Group (HEDG) Working Papers 16/25, HEDG, c/o Department of Economics, University of York.

    Cited by:

    1. Olsen, Kim Rose & Laudicella, Mauro, 2019. "Health care inequality in free access health systems: The impact of non-pecuniary incentives on diabetic patients in Danish general practices," Social Science & Medicine, Elsevier, vol. 230(C), pages 174-183.

  12. Huber, Martin & Mellace, Giovanni & Lechner, Michael, 2014. "The finite sample performance of estimators for mediation analysis under sequential conditional independence," Economics Working Paper Series 1415, University of St. Gallen, School of Economics and Political Science, revised Nov 2014.

    Cited by:

    1. Arun Advani & Toru Kitagawa & Tymon S{l}oczy'nski, 2018. "Mostly Harmless Simulations? Using Monte Carlo Studies for Estimator Selection," Papers 1809.09527, arXiv.org, revised Apr 2019.
    2. Hugo Bodory & Martin Huber & Michael Lechner, 2022. "The finite sample performance of instrumental variable-based estimators of the Local Average Treatment Effect when controlling for covariates," Papers 2212.07379, arXiv.org.
    3. Arun Advani & Toru Kitagawa & Tymon Sloczynski, 2018. "Mostly harmless simulations? On the internal validity of empirical Monte Carlo studies," CeMMAP working papers CWP56/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Lombardi, Stefano & van den Berg, Gerard J. & Vikström, Johan, 2021. "Empirical Monte Carlo Evidence on Estimation of Timing-of-Events Models," IZA Discussion Papers 14015, IZA Network @ LISER.
    5. Giovanni Mellace & Alessandra Pasquini, 2019. "Identify More, Observe Less: Mediation Analysis Synthetic Control," CEIS Research Paper 474, Tor Vergata University, CEIS, revised 20 Nov 2019.
    6. Frölich, Markus & Huber, Martin & Wiesenfarth, Manuel, 2017. "The finite sample performance of semi- and non-parametric estimators for treatment effects and policy evaluation," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 91-102.
    7. Mellace, Giovanni & Pasquini, Alessandra, 2019. "Identify More, Observe Less: Mediation Analysis: Mediation Analysis Synthetic Control," Discussion Papers on Economics 12/2019, University of Southern Denmark, Department of Economics.
    8. Harold Kincaid, 2025. "Statistical variable selection and causality in the social and behavioral sciences," Quality & Quantity: International Journal of Methodology, Springer, vol. 59(2), pages 1383-1404, April.
    9. Bijwaard, Govert & Alessie, Rob & Angelini, Viola, 2018. "The Effect of Early Life Health on Later Life Home Care Use: The Mediating Role of Household Composition," IZA Discussion Papers 11729, IZA Network @ LISER.
    10. Giovanni Mellace & Alessandra Pasquini, 2022. "Mediation Analysis Synthetic Control," Temi di discussione (Economic working papers) 1389, Bank of Italy, Economic Research and International Relations Area.
    11. Ren, Qianping & Wang, Liyan & Ye, Maoliang, 2025. "Long-term impacts of early adversity on subjective well-being: Evidence from the Chinese great famine," Journal of Economic Behavior & Organization, Elsevier, vol. 230(C).
    12. Bodory, Hugo & Huber, Martin & Camponovo, Lorenzo & Lechner, Michael, 2016. "The finite sample performance of inference methods for propensity score matching and weighting estimators," FSES Working Papers 466, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    13. Bellani, Luna & Bia, Michela Gianna, 2017. "The Long-Run Impact of Childhood Poverty and the Mediating Role of Education," IZA Discussion Papers 10677, IZA Network @ LISER.
    14. Stephen Whelan, 2017. "Does homeownership affect education outcomes?," World of Labour, LISER, pages 342-342, April.

  13. Huber, Martin & Mellace, Giovanni & Lechner, Michael, 2014. "Why do tougher caseworkers increase employment? The role of programme assignment as a causal mechanism," Economics Working Paper Series 1414, University of St. Gallen, School of Economics and Political Science.

    Cited by:

    1. Lechner, Michael, 2019. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," CEPR Discussion Papers 13430, Centre for Economic Policy Research.
    2. Strobl, Renate & Wunsch, Conny, 2018. "Identification of causal mechanisms based on between-subject double randomization designs," Working papers 2018/19, Faculty of Business and Economics - University of Basel.
    3. Huber, Martin & Lechner, Michael & Strittmatter, Anthony, 2015. "Direct and indirect effects of training vouchers for the unemployed," Economics Working Paper Series 1514, University of St. Gallen, School of Economics and Political Science.
    4. Kyyrä, Tomi & Verho, Jouko Kullervo, 2025. "Do Financial Incentives for Training and Caseworker Meetings Enhance Re-Employment?," IZA Discussion Papers 17881, IZA Network @ LISER.
    5. Wifo, 2017. "WIFO-Monatsberichte, Heft 6/2017," WIFO Monatsberichte (monthly reports), WIFO, vol. 90(6), June.
    6. Halling, Aske & Petersen, Niels Bjørn Grund, 2024. "Frontline Employees’ Responses to Citizens’ Communication of Administrative Burdens," OSF Preprints yqzg4, Center for Open Science.
    7. Knaus, Michael C. & Lechner, Michael & Strittmatter, Anthony, 2018. "Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence," IZA Discussion Papers 12039, IZA Network @ LISER.
    8. Vikström, Johan & Söderström, Martin & Cederlöf, Jonas, 2021. "What makes a good caseworker?," Working Paper Series 2021:9, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    9. Knaus, Michael C., 2020. "Double Machine Learning based Program Evaluation under Unconfoundedness," Economics Working Paper Series 2004, University of St. Gallen, School of Economics and Political Science.
    10. Viviana Celli, 2022. "Causal mediation analysis in economics: Objectives, assumptions, models," Journal of Economic Surveys, Wiley Blackwell, vol. 36(1), pages 214-234, February.
    11. Prifti, Ervin & Daidone, Silvio & Davis, Benjamin, 2019. "Causal pathways of the productive impacts of cash transfers: Experimental evidence from Lesotho," World Development, Elsevier, vol. 115(C), pages 258-268.
    12. Farbmacher, Helmut & Huber, Martin & Langen, Henrika & Spindler, Martin, 2020. "Causal mediation analysis with double machine learning," FSES Working Papers 515, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    13. Michael Lechner & Jana Mareckova, 2022. "Modified Causal Forest," Papers 2209.03744, arXiv.org.
    14. Homrighausen, Pia & Oberfichtner, Michael, 2026. "Do caseworker meetings prevent unemployment? Evidence from a field experiment," European Economic Review, Elsevier, vol. 183(C).
    15. Knaus, Michael C. & Lechner, Michael & Strittmatter, Anthony, 2017. "Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach," Economics Working Paper Series 1711, University of St. Gallen, School of Economics and Political Science.
    16. Huber, Martin & Laffers, Lukáš, 2020. "Bounds on direct and indirect effects under treatment/mediator endogeneity and outcome attrition," FSES Working Papers 514, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    17. Ulrike Huemer & Rainer Eppel & Marion Kogler & Helmut Mahringer & Lukas Schmoigl & David Pichler, 2021. "Effektivität von Instrumenten der aktiven Arbeitsmarktpolitik in unterschiedlichen Konjunkturphasen," WIFO Studies, WIFO, number 67250.
    18. Ville Vehkasalo, 2020. "Effects of face-to-face counselling on unemployment rate and duration: evidence from a Public Employment Service reform," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 54(1), pages 1-14, December.
    19. Frölich, Markus & Huber, Martin & Wiesenfarth, Manuel, 2017. "The finite sample performance of semi- and non-parametric estimators for treatment effects and policy evaluation," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 91-102.
    20. Schiprowski, Amelie, 2017. "The Role of Caseworkers in Unemployment Insurance: Evidence from Unplanned Absences," IZA Discussion Papers 11040, IZA Network @ LISER.
    21. Doerr Annabelle & Strittmatter Anthony, 2021. "Identifying Causal Channels of Policy Reforms with Multiple Treatments and Different Types of Selection," Journal of Econometric Methods, De Gruyter, vol. 10(1), pages 67-88, January.
    22. René Böheim & Rainer Eppel & Helmut Mahringer, 2023. "The impact of lower caseloads in public employment services on the unemployed," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 57(1), pages 1-25, December.
    23. Viviana Celli, 2019. "Causal Mediation Analysis in Economics: objectives, assumptions, models," Working Papers 12/19, Sapienza University of Rome, DISS.
    24. Martin Huber & Mark Schelker & Anthony Strittmatter, 2019. "Direct and Indirect Effects Based on Changes-in-Changes," CESifo Working Paper Series 7855, CESifo.
    25. Schütt, Christoph A., 2023. "The effect of perceived similarity and social proximity on the formation of prosocial preferences," Journal of Economic Psychology, Elsevier, vol. 99(C).
    26. Huber, Martin & Mellace, Giovanni & Lechner, Michael, 2014. "The finite sample performance of estimators for mediation analysis under sequential conditional independence," Economics Working Paper Series 1415, University of St. Gallen, School of Economics and Political Science, revised Nov 2014.
    27. Nora Bearth & Michael Lechner & Jana Mareckova & Fabian Muny, 2025. "Fairness-Aware and Interpretable Policy Learning," Papers 2509.12119, arXiv.org.
    28. David G. Lugo‐Palacios & Jonathan M. Clarke & Søren Rud Kristensen, 2023. "Back to basics: A mediation analysis approach to addressing the fundamental questions of integrated care evaluations," Health Economics, John Wiley & Sons, Ltd., vol. 32(9), pages 2080-2097, September.
    29. Xinxin Ma, 2024. "Political party affiliation and subjective well‐being: Evidence from China," Asian Economic Journal, East Asian Economic Association, vol. 38(4), pages 507-532, December.
    30. Steinmayr, Andreas, 2014. "When a random sample is not random: Bounds on the effect of migration on household members left behind," Kiel Working Papers 1975, Kiel Institute for the World Economy.
    31. Rainer Eppel & Helmut Mahringer & Petra Sauer, 2017. "Österreich 2025 – Arbeitslosigkeit und die Rolle der aktiven Arbeitsmarktpolitik," WIFO Monatsberichte (monthly reports), WIFO, vol. 90(6), pages 493-505, June.
    32. Joachim Wilde, 2022. "What drives trust of the long‐term unemployed in their caseworkers?," LABOUR, CEIS, vol. 36(2), pages 231-250, June.
    33. Martin Huber & Anna Solovyeva, 2020. "On the Sensitivity of Wage Gap Decompositions," Journal of Labor Research, Springer, vol. 41(1), pages 1-33, June.

  14. Huber, Martin & Mellace, Giovanni, 2012. "Relaxing monotonicity in the identification of local average treatment effects," Economics Working Paper Series 1212, University of St. Gallen, School of Economics and Political Science.

    Cited by:

    1. Clément de Chaisemartin, 2012. "Late again, whithout Monotonicity," Working Papers 2012-12, Center for Research in Economics and Statistics.
    2. Will Dobbie & Jae Song, 2014. "Debt Relief and Debtor Outcomes: Measuring the Effects of Consumer Bankruptcy Protection," NBER Working Papers 20520, National Bureau of Economic Research, Inc.
    3. Clément de Chaisemartin & Xavier d'Haultfoeuille, 2012. "Late Again with Defiers," Working Papers halshs-00699646, HAL.
    4. Fiorini, Mario & Katrien Stevens, 2014. "Assessing the Monotonicity Assumption in IV and fuzzy RD designs," Working Papers 2014-13, University of Sydney, School of Economics.

  15. Giovanni Mellace & Roberto Rocci, 2011. "Principal Stratification in sample selection problems with non normal error terms," CEIS Research Paper 194, Tor Vergata University, CEIS, revised 02 May 2011.

    Cited by:

    1. Huber, Martin & Mellace, Giovanni, 2011. "Sharp bounds on causal effects under sample selection," Economics Working Paper Series 1134, University of St. Gallen, School of Economics and Political Science.
    2. Xiaolin Sun & Xueyan Zhao & D. S. Poskitt, 2024. "Partially Identified Heterogeneous Treatment Effect with Selection: An Application to Gender Gaps," Papers 2410.01159, arXiv.org, revised Oct 2024.

  16. Huber, Martin & Mellace, Giovanni, 2011. "Sharp bounds on causal effects under sample selection," Economics Working Paper Series 1134, University of St. Gallen, School of Economics and Political Science.

    Cited by:

    1. Huber, Martin & Melly, Blaise, 2011. "Quantile Regression in the Presence of Sample Selection," Economics Working Paper Series 1109, University of St. Gallen, School of Economics and Political Science.
    2. Bartalotti, Otávio & Kédagni, Désiré & Possebom, Vitor, 2023. "Identifying marginal treatment effects in the presence of sample selection," Journal of Econometrics, Elsevier, vol. 234(2), pages 565-584.
    3. Blanco, German & Chen, Xuan & Flores, Carlos A. & Flores-Lagunes, Alfonso, 2018. "Bounds on Average and Quantile Treatment Effects on Duration Outcomes under Censoring, Selection, and Noncompliance," GLO Discussion Paper Series 288, Global Labor Organization (GLO).
    4. Phillip Heiler & Asbj{o}rn Kaufmann & Bezirgen Veliyev, 2024. "Treatment Evaluation at the Intensive and Extensive Margins," Papers 2412.11179, arXiv.org.
    5. Kitagawa, Toru, 2021. "The identification region of the potential outcome distributions under instrument independence," Journal of Econometrics, Elsevier, vol. 225(2), pages 231-253.
    6. Lutz Depenbusch & Pepijn Schreinemachers & Stuart Brown & Ralph Roothaert, 2022. "Impact and distributional effects of a home garden and nutrition intervention in Cambodia," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 14(4), pages 865-881, August.
    7. Xiaolin Sun & Xueyan Zhao & D. S. Poskitt, 2024. "Partially Identified Heterogeneous Treatment Effect with Selection: An Application to Gender Gaps," Papers 2410.01159, arXiv.org, revised Oct 2024.
    8. Phillip Heiler, 2022. "Heterogeneous Treatment Effect Bounds under Sample Selection with an Application to the Effects of Social Media on Political Polarization," Papers 2209.04329, arXiv.org, revised Jul 2024.
    9. Possebom, Vitor, 2018. "Sharp bounds on the MTE with sample selection," MPRA Paper 89785, University Library of Munich, Germany.
    10. Martin Huber & Giovanni Mellace, 2010. "Sharp IV bounds on average treatment effects under endogeneity and noncompliance," University of St. Gallen Department of Economics working paper series 2010 2010-31, Department of Economics, University of St. Gallen.
    11. Daniel Brüggmann, 2020. "Women’s employment, income and divorce in West Germany: a causal approach," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 54(1), pages 1-22, December.

  17. Huber, Martin & Mellace, Giovanni, 2011. "Testing instrument validity for LATE identification based on inequality moment constraints," Economics Working Paper Series 1143, University of St. Gallen, School of Economics and Political Science.

    Cited by:

    1. Black, Dan A. & Joo, Joonhwi & LaLonde, Robert & Smith, Jeffrey A. & Taylor, Evan J., 2022. "Simple Tests for Selection: Learning More from Instrumental Variables," Labour Economics, Elsevier, vol. 79(C).
    2. Kedagni, Desire, 2018. "Identifying Treatment Effects in the Presence of Confounded Types," ISU General Staff Papers 201809110700001056, Iowa State University, Department of Economics.
    3. Darío Tortarolo, 2014. "Female Labor Supply and Fertility. Causal Evidence for Latin America," CEDLAS, Working Papers 0166, CEDLAS, Universidad Nacional de La Plata.
    4. Ismael Mourifie & Yuanyuan Wan, 2014. "Testing Local Average Treatment Effect Assumptions," Working Papers tecipa-514, University of Toronto, Department of Economics.
    5. Thomas Carr & Toru Kitagawa, 2021. "Testing Instrument Validity with Covariates," Papers 2112.08092, arXiv.org, revised Sep 2023.
    6. Patrick Kline & Christopher R. Walters, 2018. "On Heckits, LATE, and Numerical Equivalence," CESifo Working Paper Series 6994, CESifo.
    7. Huber, Martin, 2019. "An introduction to flexible methods for policy evaluation," FSES Working Papers 504, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    8. Fricke, Hans & Frölich, Markus & Huber, Martin & Lechner, Michael, 2015. "Endogeneity and non-response bias in treatment evaluation - nonparametric identification of causal effects by instruments," FSES Working Papers 459, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    9. Wang, Xintong & Flores-Lagunes, Alfonso, 2020. "Conscription and Military Service: Do They Result in Future Violent and Non-Violent Incarcerations and Recidivism?," IZA Discussion Papers 14003, IZA Network @ LISER.
    10. Hongyi Jiang & Zhenting Sun, 2023. "Testing Partial Instrument Monotonicity," Papers 2308.08390, arXiv.org, revised Aug 2023.
    11. Santiago Acerenza & Otávio Bartalotti & Désiré Kédagni, 2023. "Testing identifying assumptions in bivariate probit models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 407-422, April.
    12. James Bisbee & Rajeev Dehejia & Cristian Pop-Eleches & Cyrus Samii, 2015. "Local Instruments, Global Extrapolation: External Validity of the Labor Supply-Fertility Local Average Treatment Effect," NBER Working Papers 21663, National Bureau of Economic Research, Inc.
    13. M. Azhar Hussain & Nikolaj Siersbæk & Lars Peter Østerdal, 2020. "Multidimensional welfare comparisons of EU member states before, during, and after the financial crisis: a dominance approach," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 55(4), pages 645-686, December.
    14. Tymon S{l}oczy'nski & S. Derya Uysal & Jeffrey M. Wooldridge, 2022. "Abadie's Kappa and Weighting Estimators of the Local Average Treatment Effect," Papers 2204.07672, arXiv.org, revised Feb 2024.
    15. Joeri Smits & Jeffrey S. Racine, 2013. "Testing Exclusion Restrictions in Nonseparable Triangular Models," Department of Economics Working Papers 2013-02, McMaster University.
    16. Nadja van 't Hoff, 2023. "Identifying Causal Effects of Discrete, Ordered and ContinuousTreatments using Multiple Instrumental Variables," Papers 2311.17575, arXiv.org, revised Oct 2024.
    17. Markus Frölich & Martin Huber, 2017. "Direct and indirect treatment effects–causal chains and mediation analysis with instrumental variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1645-1666, November.
    18. Rui Wang, 2023. "Point Identification of LATE with Two Imperfect Instruments," Papers 2303.13795, arXiv.org.
    19. Mario Fiorini & Katrien Stevens, 2021. "Scrutinizing the Monotonicity Assumption in IV and fuzzy RD designs," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(6), pages 1475-1526, December.
    20. Johann Caro-Burnett, 2026. "Toggling the Defiers to Relax Monotonicity: The Difference-in-Instrumental-Variables Estimand," Papers 2602.12504, arXiv.org.
    21. Blaise Melly und Kaspar W thrich, 2016. "Local quantile treatment effects," Diskussionsschriften dp1605, Universitaet Bern, Departement Volkswirtschaft.
    22. María Angélica Bautista & Felipe González & Luis R. Martínez & Pablo Muñoz & Mounu Prem, 2019. "The Geography of Dictatorship and Support for Democracy," HiCN Working Papers 298, Households in Conflict Network.
    23. Martin Huber & Giovanni Mellace, 2014. "Testing exclusion restrictions and additive separability in sample selection models," Empirical Economics, Springer, vol. 47(1), pages 75-92, August.
    24. Bolzern, Benjamin & Huber, Martin, 2017. "Testing the validity of the compulsory schooling law instrument," Economics Letters, Elsevier, vol. 159(C), pages 23-27.
    25. Krumme, Anna & Westphal, Matthias, 2026. "Testing IV Validity and LATE Interpretation Using Flexible Covariate Specifications," IZA Discussion Papers 18573, IZA Network @ LISER.
    26. Julia Schmieder, 2020. "Fertility as a Driver of Maternal Employment," Discussion Papers of DIW Berlin 1882, DIW Berlin, German Institute for Economic Research.
    27. Luther Yap, 2025. "Sensitivity of Policy‐Relevant Treatment Parameters to Violations of Monotonicity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 40(6), pages 627-638, September.
    28. Jan Priebe, 2020. "Quasi-experimental evidence for the causal link between fertility and subjective well-being," Journal of Population Economics, Springer;European Society for Population Economics, vol. 33(3), pages 839-882, July.
    29. Mohamed Coulibaly & Yu-Chin Hsu & Ismael Mourifie & Yuanyuan Wan, 2024. "A Sharp Test for the Judge Leniency Design," Working Papers tecipa-774, University of Toronto, Department of Economics.
    30. Salm, Martin & Siflinger, Bettina & Xie, Mingjia, 2021. "The Effect of Retirement on Mental Health: Indirect Treatment Effects and Causal Mediation," Other publications TiSEM e28efa7f-8219-437c-a26d-2, Tilburg University, School of Economics and Management.
    31. Mourifié, Ismael & Wan, Yuanyuan, 2025. "Layered policy analysis in program evaluation using the marginal treatment effect," Journal of Econometrics, Elsevier, vol. 251(C).
    32. Kitagawa, Toru, 2021. "The identification region of the potential outcome distributions under instrument independence," Journal of Econometrics, Elsevier, vol. 225(2), pages 231-253.
    33. Christina Felfe & Martin Huber, 2017. "Does preschool boost the development of minority children?: the case of Roma children," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(2), pages 475-502, February.
    34. Frölich, Markus & Huber, Martin, 2014. "Treatment Evaluation with Multiple Outcome Periods under Endogeneity and Attrition," IZA Discussion Papers 7972, IZA Network @ LISER.
    35. Eduardo Fé, 2021. "Pension eligibility rules and the local causal effect of retirement on cognitive functioning," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(3), pages 812-841, July.
    36. Carmen Aina & Daniela Sonedda, 2022. "Sooner or later? The impact of child education on household consumption," Empirical Economics, Springer, vol. 63(4), pages 2071-2099, October.
    37. Machado, Cecilia & Shaikh, Azeem M. & Vytlacil, Edward J., 2019. "Instrumental variables and the sign of the average treatment effect," Journal of Econometrics, Elsevier, vol. 212(2), pages 522-555.
    38. Evan K. Rose & Yotam Shem-Tov, 2021. "On Recoding Ordered Treatments as Binary Indicators," Papers 2111.12258, arXiv.org, revised Mar 2024.
    39. Hofmarcher, Thomas, 2021. "The effect of education on poverty: A European perspective," Economics of Education Review, Elsevier, vol. 83(C).
    40. Toru Kitagawa, 2013. "A bootstrap test for instrument validity in heterogeneous treatment effect models," CeMMAP working papers CWP53/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    41. Brigham R. Frandsen & Lars J. Lefgren & Emily C. Leslie, 2019. "Judging Judge Fixed Effects," NBER Working Papers 25528, National Bureau of Economic Research, Inc.
    42. Dahl, Christian M. & Huber, Martin & Mellace, Giovanni, 2017. "It's never too LATE: A new look at local average treatment effects with or without defiers," Discussion Papers on Economics 2/2017, University of Southern Denmark, Department of Economics.
    43. Huber Martin & Wüthrich Kaspar, 2019. "Local Average and Quantile Treatment Effects Under Endogeneity: A Review," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-27, January.
    44. Ricardo E. Miranda, 2026. "On the falsification of instrumental variable models for heterogeneous treatment effects," Papers 2601.14464, arXiv.org.
    45. Tymon Stoczyński & Liang Sun & S. Derya Uysal & Derya Uysal, 2026. "A Practical Guide to Instrumental Variables Methods with Heterogeneous Treatment Effects," CESifo Working Paper Series 12696, CESifo.
    46. Bjerk, David J., 2025. "Understanding IV Versus OLS Estimates of Treatment Effects and the Coefficient Difference Check," IZA Discussion Papers 18274, IZA Network @ LISER.
    47. Sarnetzki, Florian & Dzemski, Andreas, 2014. "Overidentification test in a nonparametric treatment model with unobserved heterogeneity," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100620, Verein für Socialpolitik / German Economic Association.
    48. Huber, Martin & Mellace, Giovanni, 2011. "Testing instrument validity in sample selection models," Economics Working Paper Series 1145, University of St. Gallen, School of Economics and Political Science.
    49. Huber, Martin & Wüthrich, Kaspar, 2017. "Evaluating local average and quantile treatment effects under endogeneity based on instruments: a review," FSES Working Papers 479, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    50. Yu-Chin Hsu & Ji-Liang Shiu & Yuanyuan Wan, 2023. "Testing Identification Conditions of LATE in Fuzzy Regression Discontinuity Designs," Working Papers tecipa-761, University of Toronto, Department of Economics.
    51. Magne Mogstad & Andres Santos & Alexander Torgovitsky, 2017. "Using Instrumental Variables for Inference about Policy Relevant Treatment Effects," NBER Working Papers 23568, National Bureau of Economic Research, Inc.
    52. Sun, Zhenting & Wüthrich, Kaspar, 2025. "Pairwise valid instruments," Journal of Econometrics, Elsevier, vol. 250(C).
    53. Stefan Tübbicke, 2023. "When to use matching and weighting or regression in instrumental variable estimation? Evidence from college proximity and returns to college," Empirical Economics, Springer, vol. 65(6), pages 2979-2999, December.
    54. Yinchu Zhu, 2021. "Phase transition of the monotonicity assumption in learning local average treatment effects," Papers 2103.13369, arXiv.org.
    55. Lukas Laffers & Giovanni Mellace, 2017. "A note on testing instrument validity for the identification of LATE," Empirical Economics, Springer, vol. 53(3), pages 1281-1286, November.
    56. Jiang, Hongyi & Sun, Zhenting, 2023. "Testing partial instrument monotonicity," Economics Letters, Elsevier, vol. 233(C).
    57. Guber, Raphael, 2018. "Instrument Validity Tests with Causal Trees: With an Application to the Same-sex Instrument," MEA discussion paper series 201805, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    58. Dionissi Aliprantis & Francisca G.-C. Richter, 2020. "Evidence of Neighborhood Effects from Moving to Opportunity: Lates of Neighborhood Quality," The Review of Economics and Statistics, MIT Press, vol. 102(4), pages 633-647, October.
    59. Joshua D. Angrist, 2022. "Empirical Strategies in Economics: Illuminating the Path From Cause to Effect," Econometrica, Econometric Society, vol. 90(6), pages 2509-2539, November.
    60. Zhenting Sun & Kaspar Wuthrich, 2022. "Pairwise Valid Instruments," Papers 2203.08050, arXiv.org, revised Feb 2025.
    61. Patrick Kline & Christopher Walters, 2015. "Evaluating Public Programs with Close Substitutes: The Case of Head Start," NBER Working Papers 21658, National Bureau of Economic Research, Inc.
    62. Francis DiTraglia & Camilo García-Jimeno, 2016. "A Framework for Eliciting, Incorporating, and Disciplining Identification Beliefs in Linear Models," NBER Working Papers 22621, National Bureau of Economic Research, Inc.
    63. Sun, Zhenting, 2023. "Instrument validity for heterogeneous causal effects," Journal of Econometrics, Elsevier, vol. 237(2).
    64. Tymon S{l}oczy'nski & Liyang Sun & S. Derya Uysal, 2026. "A Practical Guide to Instrumental Variables Methods with Heterogeneous Treatment Effects," Papers 2605.15115, arXiv.org.
    65. G. Brunello & M. Fort & G. Weber & C. T. Weiss, 2013. "Testing the Internal Validity of Compulsory School Reforms as Instrument for Years of Schooling," Working Papers wp911, Dipartimento Scienze Economiche, Universita' di Bologna.
    66. Linbo Wang & James M. Robins & Thomas S. Richardson, 2017. "On falsification of the binary instrumental variable model," Biometrika, Biometrika Trust, vol. 104(1), pages 229-236.
    67. Carolina Castagnetti & Luisa Rosti & Marina Töpfer, 2020. "Discriminate me — If you can! The disappearance of the gender pay gap among public‐contest selected employees in Italy," Gender, Work and Organization, Wiley Blackwell, vol. 27(6), pages 1040-1076, November.
    68. Eckhoff Andresen, Martin & Huber, Martin, 2018. "Instrument-based estimation with binarized treatments: Issues and tests for the exclusion restriction," FSES Working Papers 492, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    69. Claudia Noack, 2021. "Sensitivity of LATE Estimates to Violations of the Monotonicity Assumption," Papers 2106.06421, arXiv.org.
    70. Öberg, Stefan, 2021. "The casual effect of fertility: The multiple problems with instrumental variables for the number of children in families," SocArXiv peuvz, Center for Open Science.

  18. Martin Huber & Giovanni Mellace, 2010. "Sharp IV bounds on average treatment effects under endogeneity and noncompliance," University of St. Gallen Department of Economics working paper series 2010 2010-31, Department of Economics, University of St. Gallen.

    Cited by:

    1. Huber, Martin & Mellace, Giovanni, 2012. "Relaxing monotonicity in the identification of local average treatment effects," Economics Working Paper Series 1212, University of St. Gallen, School of Economics and Political Science.
    2. Huber, Martin, 2012. "Statistical verification of a natural "natural experiment": Tests and sensitivity checks for the sibling sex ratio instrument," Economics Working Paper Series 1219, University of St. Gallen, School of Economics and Political Science.
    3. Amanda E. Kowalski, 2016. "Doing More When You're Running LATE: Applying Marginal Treatment Effect Methods to Examine Treatment Effect Heterogeneity in Experiments," NBER Working Papers 22363, National Bureau of Economic Research, Inc.
    4. Chen, Xuan & Flores, Carlos A. & Flores-Lagunes, Alfonso, 2015. "Going Beyond LATE: Bounding Average Treatment Effects of Job Corps Training," IZA Discussion Papers 9511, IZA Network @ LISER.
    5. Amanda E. Kowalski, 2016. "Doing More When You're Running LATE: Applying Marginal Treatment Effect Methods to Examine Treatment Effect Heterogeneity in Experiments for the Young and Privately Insured"," Cowles Foundation Discussion Papers 2045, Cowles Foundation for Research in Economics, Yale University.
    6. Laffers, Lukas & Mellace, Giovanni, 2015. "A Note on Testing the LATE Assumptions," Discussion Papers on Economics 4/2015, University of Southern Denmark, Department of Economics.
    7. Huber, Martin & Mellace, Giovanni, 2011. "Testing instrument validity for LATE identification based on inequality moment constraints," Economics Working Paper Series 1143, University of St. Gallen, School of Economics and Political Science.
    8. Huber, Martin & Wüthrich, Kaspar, 2017. "Evaluating local average and quantile treatment effects under endogeneity based on instruments: a review," FSES Working Papers 479, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    9. Murard, Elie, 2019. "The Impact of Migration on Family Left Behind: Estimation in Presence of Intra-Household Selection of Migrants," IZA Discussion Papers 12094, IZA Network @ LISER.
    10. Steinmayr, Andreas, 2014. "When a random sample is not random: Bounds on the effect of migration on household members left behind," Kiel Working Papers 1975, Kiel Institute for the World Economy.

Articles

  1. Di Francesco, Riccardo & Mellace, Giovanni, 2025. "Causal inference for qualitative outcomes," Economics Letters, Elsevier, vol. 256(C).
    See citations under working paper version above.
  2. Federico Crudu & Michael C. Knaus & Giovanni Mellace & Joeri Smits, 2025. "On the role of the zero conditional mean assumption for causal inference in linear models," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 58(4), pages 1377-1390, November.
    See citations under working paper version above.
  3. Christian M Dahl & Martin Huber & Giovanni Mellace, 2023. "It is never too LATE: a new look at local average treatment effects with or without defiers," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 378-404.
    See citations under working paper version above.
  4. Mellace, Giovanni & Ventura, Marco, 2023. "The short-run effects of public incentives for innovation in Italy," Economic Modelling, Elsevier, vol. 120(C).

    Cited by:

    1. Vanessa Cirulli & Giuliano Resce & Marco Ventura, 2024. "Co-payment exemption and healthcare consumption: quasi-experimental evidence from Italy," Empirical Economics, Springer, vol. 67(1), pages 355-380, July.
    2. He, Siyi & Liu, Jinsong & Ying, Qianwei, 2023. "Externalities of government-oriented support for innovation: Evidence from the national innovative city pilot policy in China," Economic Modelling, Elsevier, vol. 128(C).

  5. Crudu, Federico & Mellace, Giovanni & Sándor, Zsolt, 2021. "Inference In Instrumental Variable Models With Heteroskedasticity And Many Instruments," Econometric Theory, Cambridge University Press, vol. 37(2), pages 281-310, April.
    See citations under working paper version above.
  6. Martin Huber & Lukas Laffers & Giovanni Mellace, 2017. "Sharp IV Bounds on Average Treatment Effects on the Treated and Other Populations Under Endogeneity and Noncompliance," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(1), pages 56-79, January.

    Cited by:

    1. Federico A. Bugni & Mengsi Gao & Filip Obradovic & Amilcar Velez, 2024. "Identification and Inference on Treatment Effects under Covariate-Adaptive Randomization and Imperfect Compliance," Papers 2406.08419, arXiv.org, revised Apr 2025.
    2. Bartalotti, Otávio & Kédagni, Désiré & Possebom, Vitor, 2023. "Identifying marginal treatment effects in the presence of sample selection," Journal of Econometrics, Elsevier, vol. 234(2), pages 565-584.
    3. Wang, Xintong & Flores-Lagunes, Alfonso, 2020. "Conscription and Military Service: Do They Result in Future Violent and Non-Violent Incarcerations and Recidivism?," IZA Discussion Papers 14003, IZA Network @ LISER.
    4. Amanda E. Kowalski, 2018. "Extrapolation using Selection and Moral Hazard Heterogeneity from within the Oregon Health Insurance Experiment," Cowles Foundation Discussion Papers 2135, Cowles Foundation for Research in Economics, Yale University.
    5. Nadja van 't Hoff, 2023. "Identifying Causal Effects of Discrete, Ordered and ContinuousTreatments using Multiple Instrumental Variables," Papers 2311.17575, arXiv.org, revised Oct 2024.
    6. Luther Yap, 2025. "Sensitivity of Policy‐Relevant Treatment Parameters to Violations of Monotonicity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 40(6), pages 627-638, September.
    7. Santiago Acerenza & Julian Martinez-Iriarte & Alejandro S'anchez-Becerra & Pietro Emilio Spini, 2025. "Bounds for within-household encouragement designs with interference," Papers 2503.14314, arXiv.org.
    8. Kitagawa, Toru, 2021. "The identification region of the potential outcome distributions under instrument independence," Journal of Econometrics, Elsevier, vol. 225(2), pages 231-253.
    9. Chen, Xuan & Flores, Carlos A. & Flores-Lagunes, Alfonso, 2015. "Going Beyond LATE: Bounding Average Treatment Effects of Job Corps Training," IZA Discussion Papers 9511, IZA Network @ LISER.
    10. Kory Kroft & Ismael Mourifi'e & Atom Vayalinkal, 2024. "Horowitz-Manski-Lee Bounds with Multilayered Sample Selection," Papers 2409.04589, arXiv.org, revised Feb 2025.
    11. Lukáš Lafférs, 2019. "Identification in Models with Discrete Variables," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 657-696, February.
    12. Xiaolin Sun & Xueyan Zhao & D. S. Poskitt, 2024. "Partially Identified Heterogeneous Treatment Effect with Selection: An Application to Gender Gaps," Papers 2410.01159, arXiv.org, revised Oct 2024.
    13. Marx, Philip, 2024. "Sharp bounds in the latent index selection model," Journal of Econometrics, Elsevier, vol. 238(2).
    14. Huber Martin & Wüthrich Kaspar, 2019. "Local Average and Quantile Treatment Effects Under Endogeneity: A Review," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-27, January.
    15. Possebom, Vitor, 2018. "Sharp bounds on the MTE with sample selection," MPRA Paper 89785, University Library of Munich, Germany.
    16. Lukas Laffers & Giovanni Mellace, 2017. "A note on testing instrument validity for the identification of LATE," Empirical Economics, Springer, vol. 53(3), pages 1281-1286, November.
    17. Christelis, Dimitris & Messina, Julián, 2019. "Partial Identification of Population Average and Quantile Treatment Effects in Observational Data under Sample Selection," IDB Publications (Working Papers) 9520, Inter-American Development Bank.
    18. Kédagni, Désiré, 2023. "Identifying treatment effects in the presence of confounded types," Journal of Econometrics, Elsevier, vol. 234(2), pages 479-511.
    19. Michela Bia & German Blanco & Marie Valentova, 2021. "The Causal Impact of Taking Parental Leave on Wages: Evidence from 2005 to 2015," LISER Working Paper Series 2021-08, Luxembourg Institute of Socio-Economic Research (LISER).
    20. Aizawa, T.;, 2019. "Reviewing the Existing Evidence of the Conditional Cash Transfer in India through the Partial Identification Approach," Health, Econometrics and Data Group (HEDG) Working Papers 19/24, HEDG, c/o Department of Economics, University of York.
    21. Claudia Noack, 2021. "Sensitivity of LATE Estimates to Violations of the Monotonicity Assumption," Papers 2106.06421, arXiv.org.
    22. Pedro Picchetti, 2026. "Sensitivity Analysis for Instrumental Variables Under Joint Relaxations of Monotonicity and Independence," Papers 2603.25529, arXiv.org, revised Mar 2026.

  7. Lukas Laffers & Giovanni Mellace, 2017. "A note on testing instrument validity for the identification of LATE," Empirical Economics, Springer, vol. 53(3), pages 1281-1286, November.

    Cited by:

    1. Thomas Carr & Toru Kitagawa, 2021. "Testing Instrument Validity with Covariates," Papers 2112.08092, arXiv.org, revised Sep 2023.
    2. Ot'avio Bartalotti & D'esir'e K'edagni & Vitor Possebom, 2021. "Identifying Marginal Treatment Effects in the Presence of Sample Selection," Papers 2112.07014, arXiv.org.
    3. Krumme, Anna & Westphal, Matthias, 2026. "Testing IV Validity and LATE Interpretation Using Flexible Covariate Specifications," IZA Discussion Papers 18573, IZA Network @ LISER.
    4. Yu-Chin Hsu & Ji-Liang Shiu & Yuanyuan Wan, 2023. "Testing Identification Conditions of LATE in Fuzzy Regression Discontinuity Designs," Working Papers tecipa-761, University of Toronto, Department of Economics.
    5. Guber, Raphael, 2018. "Instrument Validity Tests with Causal Trees: With an Application to the Same-sex Instrument," MEA discussion paper series 201805, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.

  8. Martin Huber & Michael Lechner & Giovanni Mellace, 2017. "Why Do Tougher Caseworkers Increase Employment? The Role of Program Assignment as a Causal Mechanism," The Review of Economics and Statistics, MIT Press, vol. 99(1), pages 180-183, March.
    See citations under working paper version above.
  9. Martin Huber & Michael Lechner & Giovanni Mellace, 2016. "The Finite Sample Performance of Estimators for Mediation Analysis Under Sequential Conditional Independence," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 139-160, January.
    See citations under working paper version above.
  10. Martin Huber & Giovanni Mellace, 2015. "Sharp Bounds on Causal Effects under Sample Selection," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(1), pages 129-151, February.
    See citations under working paper version above.
  11. Martin Huber & Giovanni Mellace, 2015. "Testing Instrument Validity for LATE Identification Based on Inequality Moment Constraints," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 398-411, May.
    See citations under working paper version above.
  12. Martin Huber & Giovanni Mellace, 2014. "Testing exclusion restrictions and additive separability in sample selection models," Empirical Economics, Springer, vol. 47(1), pages 75-92, August.

    Cited by:

    1. Antonio Paolo & Aysit Tansel, 2019. "English skills, labour market status and earnings of Turkish women," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 46(4), pages 669-690, November.
    2. Frölich, Markus & Huber, Martin, 2014. "Direct and Indirect Treatment Effects: Causal Chains and Mediation Analysis with Instrumental Variables," IZA Discussion Papers 8280, IZA Network @ LISER.
    3. Chunbei Wang & Le Wang, 2017. "Knot yet: minimum marriage age law, marriage delay, and earnings," Journal of Population Economics, Springer;European Society for Population Economics, vol. 30(3), pages 771-804, July.
    4. Patrinos, Harry Anthony & Psacharopoulos, George & Tansel, Aysit, 2019. "Returns to Investment in Education: The Case of Turkey," MPRA Paper 92933, University Library of Munich, Germany.
    5. World Bank, 2024. "Dominican Republic Gender Assessment [Diagnóstico Sobre Igualdad de Género en República Dominicana]," World Bank Publications - Reports 40915, The World Bank Group.
    6. Thomas Bolli & Katherine Caves & Maria Esther Oswald-Egg, 2021. "Valuable Experience: How University Internships Affect Graduates’ Income," Research in Higher Education, Springer;Association for Institutional Research, vol. 62(8), pages 1198-1247, December.
    7. Abby Alpert & David Powell, 2020. "Estimating Intensive And Extensive Tax Responsiveness," Economic Inquiry, Western Economic Association International, vol. 58(4), pages 1855-1873, October.
    8. Kenza Elass, 2022. "The multiple dimensions of selection into employment," AMSE Working Papers 2219, Aix-Marseille School of Economics, France.
    9. Jaehee Hwang, 2022. "Who Becomes a Fisherman? A Two-Stage Sample Selection Analysis on Small-Scale Fishery Choice and Income in Korea," Sustainability, MDPI, vol. 14(4), pages 1-21, February.
    10. Andrew Beauchamp & Geoffrey Sanzenbacher & Shannon Seitz & Meghan Skira, 2014. "Deadbeat Dads," Boston College Working Papers in Economics 859, Boston College Department of Economics.
    11. Christian K. Darko & Kennedy K. Abrokwa, 2020. "Do you really need it? Educational mismatch and earnings in Ghana," Review of Development Economics, Wiley Blackwell, vol. 24(4), pages 1365-1392, November.
    12. Jacobs, Josephine C. & Van Houtven, Courtney H. & Laporte, Audrey & Coyte, Peter C., 2015. "Baby Boomer caregivers in the workforce: Do they fare better or worse than their predecessors?," The Journal of the Economics of Ageing, Elsevier, vol. 6(C), pages 89-101.
    13. Brendon McConnell, 2022. "Racial Sentencing Disparities and Differential Progression Through the Criminal Justice System: Evidence From Linked Federal and State Court Data," Papers 2203.14282, arXiv.org, revised Apr 2022.
    14. Hermes, Henning & Krauß, Marina & Lergetporer, Philipp & Peter, Frauke & Wiederhold, Simon, 2022. "Early Child Care and Labor Supply of Lower-SES Mothers: A Randomized Controlled Trial," IZA Discussion Papers 15814, IZA Network @ LISER.
    15. Biavaschi, Costanza, 2016. "Recovering the counterfactual wage distribution with selective return migration," Labour Economics, Elsevier, vol. 38(C), pages 59-80.
    16. Castagnetti, Carolina & Rosti, Luisa & Töpfer, Marina, 2018. "Discriminate me - if you can! The disappearance of the gender pay gap among public-contest selected employees," Discussion Papers 103, Friedrich-Alexander University Erlangen-Nuremberg, Chair of Labour and Regional Economics.
    17. Feng‐Yi Lin & Shen‐Ho Chang & Shaio‐Yan Huang & Teng‐Shih Wang, 2021. "Self‐interested board of director and stock price crash risk in loss‐making firms," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(2), pages 2853-2890, June.
    18. Harry Anthony Patrinos & George Psacharopoulos & Aysit Tansel, 2019. "GLOBALISATION AND GOVERNANCE: Returns to Investment in Education: The Case of Turkey," ERC Working Papers 1903, ERC - Economic Research Center, Middle East Technical University, revised Mar 2019.
    19. Xiaolin Sun & Xueyan Zhao & D. S. Poskitt, 2024. "Partially Identified Heterogeneous Treatment Effect with Selection: An Application to Gender Gaps," Papers 2410.01159, arXiv.org, revised Oct 2024.
    20. Kenza Elass, 2022. "The multiple dimensions of selection into employment," French Stata Users' Group Meetings 2022 06, Stata Users Group.
    21. Kenza Elass, 2022. "The multiple dimensions of selection into employment," Working Papers hal-03788508, HAL.
    22. Dante Contreras & Roberto Gillmore & Esteban Puentes, 2017. "Self‐Employment and Queues for Wage Work: Evidence from Chile," Journal of International Development, John Wiley & Sons, Ltd., vol. 29(4), pages 473-499, May.
    23. Biewen, Martin & Fitzenberger, Bernd & Seckler, Matthias, 2020. "Counterfactual quantile decompositions with selection correction taking into account Huber/Melly (2015): An application to the German gender wage gap," Labour Economics, Elsevier, vol. 67(C).
    24. Elass, Kenza, 2024. "Male and female selection effects on gender wage gaps in three countries," Labour Economics, Elsevier, vol. 87(C).
    25. Liang, Qingquan & Zeng, Weihong & Yi, Lan, 2025. "Examining the wage gap between public and non-public sector in China: Insights from a robust quantile selection model," China Economic Review, Elsevier, vol. 92(C).
    26. Masayuki Hirukawa & Di Liu & Irina Murtazashvili & Artem Prokhorov, 2023. "DS-HECK: double-lasso estimation of Heckman selection model," Empirical Economics, Springer, vol. 64(6), pages 3167-3195, June.
    27. Maasoumi, Esfandiar & Wang, Le, 2017. "What can we learn about the racial gap in the presence of sample selection?," Journal of Econometrics, Elsevier, vol. 199(2), pages 117-130.
    28. Ahn, Soojung & Steinbach, Sandro, 2021. "COVID-19 Trade Actions in the Agricultural and Food Sector," Journal of Food Distribution Research, Food Distribution Research Society, vol. 52(2), July.
    29. Sergio Garbay & Raquel Barrera, 2021. "¿Mujeres en suelos pegajosos? Un análisis de la evolución de las distribuciones de ingresos laborales en Bolivia en el periodo 2011-2019," Revista Latinoamericana de Desarrollo Economico, Carrera de Economía de la Universidad Católica Boliviana (UCB), issue 36, pages 123-168.
    30. Grenet, Julien & Grönqvist, Hans & Niknami, Susan, 2025. "The effects of electronic monitoring on offenders and their families," Working Paper Series 2025:12, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    31. Dhamija, Gaurav & Roychowdhury, Punarjit, 2018. "The impact of women's age at marriage on own and spousal labor market outcomes in India: causation or selection?," MPRA Paper 86686, University Library of Munich, Germany.
    32. Henning Hermes & Marina Krauß & Philipp Lergetporer & Frauke Peter & Simon Wiederhold, 2025. "Early Child Care, Maternal Labor Supply, and Gender Equality: A Randomized Controlled Trial," Working Papers 2024-023, Human Capital and Economic Opportunity Working Group.
    33. Carolina Castagnetti & Luisa Rosti & Marina Töpfer, 2020. "Discriminate me — If you can! The disappearance of the gender pay gap among public‐contest selected employees in Italy," Gender, Work and Organization, Wiley Blackwell, vol. 27(6), pages 1040-1076, November.
    34. Martin Huber & Anna Solovyeva, 2020. "On the Sensitivity of Wage Gap Decompositions," Journal of Labor Research, Springer, vol. 41(1), pages 1-33, June.
    35. María Eugenia Echeberría, 2024. "Female selection into employment along the earnings distribution," Documentos de Trabajo (working papers) 24-08, Instituto de Economía - IECON.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.