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Michael C. Knaus

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. 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).

  2. 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.

  3. Phillip Heiler & Michael C. Knaus, 2021. "Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments," Papers 2110.01427, arXiv.org, revised Aug 2023.

    Cited by:

    1. Patrick Rehill & Nicholas Biddle, 2023. "Fairness Implications of Heterogeneous Treatment Effect Estimation with Machine Learning Methods in Policy-making," Papers 2309.00805, arXiv.org.
    2. Dan A. Black & Lars Skipper & Jeffrey A. Smith & Jeffrey Andrew Smith, 2023. "Firm Training," CESifo Working Paper Series 10268, CESifo.
    3. Phillip Heiler & Asbj{o}rn Kaufmann & Bezirgen Veliyev, 2024. "Treatment Evaluation at the Intensive and Extensive Margins," Papers 2412.11179, arXiv.org.

  4. Michael C. Knaus, 2020. "Double Machine Learning based Program Evaluation under Unconfoundedness," Papers 2003.03191, arXiv.org, revised Jun 2022.

    Cited by:

    1. Zheng, Yi & Ren, He, 2024. "COVID-19 vaccination and housing payments," Journal of Housing Economics, Elsevier, vol. 64(C).
    2. Sallin, Aurelién, 2021. "Estimating returns to special education: combining machine learning and text analysis to address confounding," Economics Working Paper Series 2109, University of St. Gallen, School of Economics and Political Science.
    3. 'Agoston Reguly, 2021. "Discovering Heterogeneous Treatment Effects in Regression Discontinuity Designs," Papers 2106.11640, arXiv.org, revised Aug 2025.
    4. Michael Lechner & Jana Mareckova, 2024. "Comprehensive Causal Machine Learning," Papers 2405.10198, arXiv.org, revised Feb 2025.
    5. Paul S. Clarke & Annalivia Polselli, 2023. "Double Machine Learning for Static Panel Models with Fixed Effects," Papers 2312.08174, arXiv.org, revised Dec 2024.
    6. Oyenubi, Adeola & Kollamparambil, Umakrishnan, 2023. "Does noncompliance with COVID-19 regulations impact the depressive symptoms of others?," Economic Modelling, Elsevier, vol. 120(C).
    7. Daniel Goller & Tamara Harrer & Michael Lechner & Joachim Wolff, 2021. "Active labour market policies for the long-term unemployed: New evidence from causal machine learning," Papers 2106.10141, arXiv.org, revised May 2023.
    8. Wunsch, Conny & Strittmatter, Anthony, 2021. "The Gender Pay Gap Revisited with Big Data: Do Methodological Choices Matter?," CEPR Discussion Papers 15840, C.E.P.R. Discussion Papers.
    9. Fabian Muny, 2025. "Evaluating Program Sequences with Double Machine Learning: An Application to Labor Market Policies," Papers 2506.11960, arXiv.org.
    10. Yong Bian & Xiqian Wang & Qin Zhang, 2023. "How Does China's Household Portfolio Selection Vary with Financial Inclusion?," Papers 2311.01206, arXiv.org.
    11. Di Liu, 2024. "Treatment-effects estimation using lasso," Chinese Stata Conference 2024 09, Stata Users Group.
    12. Jonathan Fuhr & Philipp Berens & Dominik Papies, 2024. "Estimating Causal Effects with Double Machine Learning -- A Method Evaluation," Papers 2403.14385, arXiv.org, revised Apr 2024.
    13. 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.
    14. Daniel Goller, 2023. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Annals of Operations Research, Springer, vol. 325(1), pages 649-679, June.
    15. Nora Bearth & Michael Lechner, 2024. "Causal Machine Learning for Moderation Effects," Papers 2401.08290, arXiv.org, revised Jan 2025.
    16. Kueck, Jannis & Luo, Ye & Spindler, Martin & Wang, Zigan, 2023. "Estimation and inference of treatment effects with L2-boosting in high-dimensional settings," Journal of Econometrics, Elsevier, vol. 234(2), pages 714-731.
    17. Nora Bearth & Michael Lechner & Jana Mareckova & Fabian Muny, 2025. "Fairness-Aware and Interpretable Policy Learning," Papers 2509.12119, arXiv.org.
    18. Ding, Yijiu & Li, Bo & Ma, Shenglin & Yu, Chunrong & Zhao, Xiaoyi, 2025. "Digital transformation and wage distortion in R&D and innovation activities - Causal inference based on double machine learning," International Review of Financial Analysis, Elsevier, vol. 106(C).
    19. Ding, Yijiu & Li, Bo & Lan, Dahai & Yu, Chunrong & Zhang, Xueqing, 2025. "Research on wage distortion in R&D and innovation activities —— Evidence from China's listed manufacturing enterprises," International Review of Economics & Finance, Elsevier, vol. 102(C).
    20. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Machine-Learning Approach," CESifo Working Paper Series 9037, CESifo.
    21. Black, Dan A. & Grogger, Jeffrey & Kirchmaier, Tom & Sanders, Koen, 2023. "Criminal Charges, Risk Assessment, and Violent Recidivism in Cases of Domestic Abuse," IZA Discussion Papers 15885, IZA Network @ LISER.
    22. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
    23. Anoop Kumar & Suresh Dodda & Navin Kamuni & Rajeev Kumar Arora, 2024. "Unveiling the Impact of Macroeconomic Policies: A Double Machine Learning Approach to Analyzing Interest Rate Effects on Financial Markets," Papers 2404.07225, arXiv.org.
    24. Julia Hatamyar & Noemi Kreif, 2023. "Policy Learning with Rare Outcomes," Papers 2302.05260, arXiv.org, revised Oct 2023.
    25. Yang, Zixuan & Yu, Huang, 2025. "Unleashing the power of Energy Saving and Emission Reduction Fiscal Policy: Balancing urban ecological resilience and efficiency," Energy, Elsevier, vol. 327(C).
    26. Wang, Xiqian & Bian, Yong & Zhang, Qin, 2023. "The effect of cooking fuel choice on the elderly’s well-being: Evidence from two non-parametric methods," Energy Economics, Elsevier, vol. 125(C).
    27. Cai, Yunhao & Jing, Peng & Wang, Baihui & Jiang, Chengxi & Wang, Yuan, 2023. "How does “over-hype” lead to public misconceptions about autonomous vehicles? A new insight applying causal inference," Transportation Research Part A: Policy and Practice, Elsevier, vol. 175(C).
    28. Phillip Heiler & Michael C. Knaus, 2021. "Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments," Papers 2110.01427, arXiv.org, revised Aug 2023.
    29. Yiyan Huang & Cheuk Hang Leung & Xing Yan & Qi Wu & Shumin Ma & Zhiri Yuan & Dongdong Wang & Zhixiang Huang, 2022. "Robust Causal Learning for the Estimation of Average Treatment Effects," Papers 2209.01805, arXiv.org.
    30. Federica Mascolo & Nora Bearth & Fabian Muny & Michael Lechner & Jana Mareckova, 2024. "From Average Effects to Targeted Assignment: A Causal Machine Learning Analysis of Swiss Active Labor Market Policies," Papers 2410.23322, arXiv.org, revised May 2025.
    31. Simon Calmar Andersen & Louise Beuchert & Phillip Heiler & Helena Skyt Nielsen, 2023. "A Guide to Impact Evaluation under Sample Selection and Missing Data: Teacher's Aides and Adolescent Mental Health," Papers 2308.04963, arXiv.org.
    32. Philipp Bach & Victor Chernozhukov & Malte S. Kurz & Martin Spindler & Sven Klaassen, 2021. "DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R," Papers 2103.09603, arXiv.org, revised Jun 2024.
    33. Mark Kattenberg & Bas Scheer & Jurre Thiel, 2023. "Causal forests with fixed effects for treatment effect heterogeneity in difference-in-differences," CPB Discussion Paper 452, CPB Netherlands Bureau for Economic Policy Analysis.
    34. Kilic, Talip & Letta, Marco & Montalbano, Pierluigi & Petruccelli, Federica, 2026. "CLARE : A Causal machine Learning Approach to Resilience Estimation," Policy Research Working Paper Series 11292, The World Bank.
    35. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Causal Machine-Learning Approach," Papers 2103.10251, arXiv.org, revised Sep 2021.
    36. Shilong Xi & Xiaohui Wang & Kejun Lin, 2025. "The Impact of Carbon Emissions Trading Pilot Policies on High-Quality Agricultural Development: An Empirical Assessment Using Double Machine Learning," Sustainability, MDPI, vol. 17(5), pages 1-28, February.
    37. Bu, Caiqi & Zhang, Kaixia, 2025. "Can decentralized energy transition policy achieve a sustainable green transformation of high-energy-consuming firms? Evidence from China," Energy Policy, Elsevier, vol. 207(C).
    38. Bonaccolto-Töpfer, Marina & Satlukal, Sascha, 2024. "Gender differences in reservation wages: New evidence for Germany," Labour Economics, Elsevier, vol. 91(C).
    39. Evan D. Peet & Dana Schultz & Susan Lovejoy & Fuchiang (Rich) Tsui, 2023. "Variation in the infant health effects of the women, infants, and children program by predicted risk using novel machine learning methods," Health Economics, John Wiley & Sons, Ltd., vol. 32(1), pages 194-217, January.
    40. Tomoko Nagai & Takayuki Okuda & Tomoya Nakamura & Yuichiro Sato & Yusuke Sato & Kensaku Kinjo & Kengo Kawamura & Shin Kikuta & Naoto Kumano-go, 2024. "Educational Effects in Mathematics: Conditional Average Treatment Effect depending on the Number of Treatments," Papers 2411.01498, arXiv.org.
    41. Luyuan Song & Xiaojun Zhang, 2024. "Estimating the Individual Treatment Effect with Different Treatment Group Sizes," Mathematics, MDPI, vol. 12(8), pages 1-17, April.
    42. Pang, Silu & Hua, Guihong, 2024. "How does digital tax administration affect R&D manipulation? Evidence from dual machine learning," Technological Forecasting and Social Change, Elsevier, vol. 208(C).
    43. Gabriel Okasa, 2022. "Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance," Papers 2201.12692, arXiv.org.
    44. Sophie M. Behr & Till Köveker & Merve Kücük, 2025. "Understanding Energy Savings in a Crisis: The Role of Prices and Non-monetary Factors," Discussion Papers of DIW Berlin 2112, DIW Berlin, German Institute for Economic Research.
    45. Bonev, Petyo & Matsumoto, Shigeru, 2022. "An empirical evaluation of environmental Alternative Dispute Resolution methods," Economics Working Paper Series 2208, University of St. Gallen, School of Economics and Political Science.
    46. Evan D. Peet & Dana Schultz & Susan Lovejoy & Fuchiang (Rich) Tsui, 2024. "The infant health effects of doulas: Leveraging big data and machine learning to inform cost‐effective targeting," Health Economics, John Wiley & Sons, Ltd., vol. 33(6), pages 1387-1411, June.
    47. Aur'elien Sallin, 2021. "Estimating returns to special education: combining machine learning and text analysis to address confounding," Papers 2110.08807, arXiv.org, revised Feb 2022.
    48. Pol Campos-Mercade & Armando N. Meier & Stephan Meier & Devin Pope & Florian H. Schneider & Erik Wengstroem, 2025. "Incentives to Vaccinate," CEBI working paper series 24-15, University of Copenhagen. Department of Economics. The Center for Economic Behavior and Inequality (CEBI).
    49. Heejun Shin & Joseph Antonelli, 2023. "Improved inference for doubly robust estimators of heterogeneous treatment effects," Biometrics, The International Biometric Society, vol. 79(4), pages 3140-3152, December.

  5. Michael C. Knaus, 2018. "A Double Machine Learning Approach to Estimate the Effects of Musical Practice on Student's Skills," Papers 1805.10300, arXiv.org, revised Jan 2019.

    Cited by:

    1. Michael Lechner & Jana Mareckova, 2024. "Comprehensive Causal Machine Learning," Papers 2405.10198, arXiv.org, revised Feb 2025.
    2. Oyenubi, Adeola & Kollamparambil, Umakrishnan, 2023. "Does noncompliance with COVID-19 regulations impact the depressive symptoms of others?," Economic Modelling, Elsevier, vol. 120(C).
    3. Knaus, Michael C., 2020. "Double Machine Learning Based Program Evaluation under Unconfoundedness," IZA Discussion Papers 13051, IZA Network @ LISER.
    4. Jonathan Fuhr & Philipp Berens & Dominik Papies, 2024. "Estimating Causal Effects with Double Machine Learning -- A Method Evaluation," Papers 2403.14385, arXiv.org, revised Apr 2024.
    5. Maximilian Maurice Gail & Phil-Adrian Klotz, 2025. "E-book Pricing Under the Agency Model: Lessons from the UK," Journal of Industry, Competition and Trade, Springer, vol. 25(1), pages 1-39, December.
    6. Huber, Martin & Meier, Jonas & Wallimann, Hannes, 2022. "Business analytics meets artificial intelligence: Assessing the demand effects of discounts on Swiss train tickets," Transportation Research Part B: Methodological, Elsevier, vol. 163(C), pages 22-39.
    7. Anna Baiardi & Andrea A. Naghi, 2024. "The effect of plough agriculture on gender roles: A machine learning approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(7), pages 1396-1402, November.
    8. Wang, Xiqian & Bian, Yong & Zhang, Qin, 2023. "The effect of cooking fuel choice on the elderly’s well-being: Evidence from two non-parametric methods," Energy Economics, Elsevier, vol. 125(C).
    9. Riccardo Di Francesco, 2022. "Aggregation Trees," CEIS Research Paper 546, Tor Vergata University, CEIS, revised 20 Nov 2023.
    10. Wang, Xu & Liu, Yingjie & Li, Wei & He, Lingyun & Chi, Cheng & Zhong, Yanni, 2025. "Market-based emissions regulation and capacity governance in China’s high-carbon firms: Theoretical investigation and empirical evidence," Economic Analysis and Policy, Elsevier, vol. 87(C), pages 1-17.
    11. Michael C. Knaus, 2024. "Treatment Effect Estimators as Weighted Outcomes," Papers 2411.11559, arXiv.org, revised Dec 2024.
    12. Philipp Bach & Victor Chernozhukov & Malte S. Kurz & Martin Spindler & Sven Klaassen, 2021. "DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R," Papers 2103.09603, arXiv.org, revised Jun 2024.
    13. Rolando Gonzales Martinez, 2021. "How good is good? Probabilistic benchmarks and nanofinance+," Papers 2103.01669, arXiv.org.
    14. Paul Hunermund & Beyers Louw & Itamar Caspi, 2021. "Double Machine Learning and Automated Confounder Selection -- A Cautionary Tale," Papers 2108.11294, arXiv.org, revised May 2023.
    15. Luyuan Song & Xiaojun Zhang, 2024. "Estimating the Individual Treatment Effect with Different Treatment Group Sizes," Mathematics, MDPI, vol. 12(8), pages 1-17, April.
    16. Smith, Jeffrey A., 2022. "Treatment Effect Heterogeneity," IZA Discussion Papers 15151, IZA Network @ LISER.
    17. Fang, Yan & Liu, Yinglin & Yang, Yi & Lucey, Brian & Abedin, Mohammad Zoynul, 2025. "How do Chinese urban investment bonds affect its economic resilience? Evidence from double machine learning," Research in International Business and Finance, Elsevier, vol. 74(C).
    18. McNamara, Sarah, 2020. "Returns to higher education and dropouts: A double machine learning approach," ZEW Discussion Papers 20-084, ZEW - Leibniz Centre for European Economic Research.

  6. Knaus, Michael C. & Lechner, Michael & Reimers, Anne K., 2018. "For Better or Worse? The Effects of Physical Education on Child Development," IZA Discussion Papers 11268, IZA Network @ LISER.

    Cited by:

    1. Steven Bednar & Kathryn Rouse, 2020. "The effect of physical education on children's body weight and human capital: New evidence from the ECLS‐K:2011," Health Economics, John Wiley & Sons, Ltd., vol. 29(4), pages 393-405, April.
    2. Packham, Analisa & Street, Brittany, 2019. "The effects of physical education on student fitness, achievement, and behavior," Economics of Education Review, Elsevier, vol. 72(C), pages 1-18.
    3. Phipps, Aaron & Amaya, Alexander, 2023. "Are students time constrained? Course load, GPA, and failing," Journal of Public Economics, Elsevier, vol. 225(C).
    4. Dimitrios Nikolaou & Laura M. Crispin, 2022. "Estimating the effects of sports and physical exercise on bullying," Contemporary Economic Policy, Western Economic Association International, vol. 40(2), pages 283-303, April.
    5. Black, Nicole & Johnston, David W. & Propper, Carol & Shields, Michael A., 2019. "The effect of school sports facilities on physical activity, health and socioeconomic status in adulthood," Social Science & Medicine, Elsevier, vol. 220(C), pages 120-128.
    6. Phillip Heiler, 2020. "Efficient Covariate Balancing for the Local Average Treatment Effect," Papers 2007.04346, arXiv.org.
    7. Nida Mugler & Hansjörg Baurecht & Kevin Lam & Michael Leitzmann & Carmen Jochem, 2022. "The Effectiveness of Interventions to Reduce Sedentary Time in Different Target Groups and Settings in Germany: Systematic Review, Meta-Analysis and Recommendations on Interventions," IJERPH, MDPI, vol. 19(16), pages 1-21, August.

  7. Goller, Daniel & Knaus, Michael C. & Lechner, Michael & Okasa, Gabriel, 2018. "Predicting Match Outcomes in Football by an Ordered Forest Estimator," Economics Working Paper Series 1811, University of St. Gallen, School of Economics and Political Science.

    Cited by:

    1. Lechner, Michael & Okasa, Gabriel, 2019. "Random Forest Estimation of the Ordered Choice Model," Economics Working Paper Series 1908, University of St. Gallen, School of Economics and Political Science.
    2. Daniel Goller, 2023. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Annals of Operations Research, Springer, vol. 325(1), pages 649-679, June.

  8. Michael C. Knaus & Michael Lechner & Anthony Strittmatter, 2018. "Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence," Papers 1810.13237, arXiv.org, revised Dec 2018.

    Cited by:

    1. Jeff Allen & Santiago Carbo-Valverde & Sujit Chakravorti & Francisco Rodriguez-Fernandez & Oya Pinar Ardic, 2022. "Assessing incentives to increase digital payment acceptance and usage: A machine learning approach," PLOS ONE, Public Library of Science, vol. 17(11), pages 1-29, November.
    2. Phillip Heiler & Michael C. Knaus, 2025. "Heterogeneity Analysis with Heterogeneous Treatments," Papers 2507.01517, arXiv.org, revised Feb 2026.
    3. Harsh Parikh & Carlos Varjao & Louise Xu & Eric Tchetgen Tchetgen, 2022. "Validating Causal Inference Methods," Papers 2202.04208, arXiv.org, revised Jul 2022.
    4. 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.
    5. Cockx, Bart & Lechner, Michael & Bollens, Joost, 2023. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," Labour Economics, Elsevier, vol. 80(C).
    6. Anthony Strittmatter & Conny Wunsch, 2025. "Labor market sorting and the gender pay gap revisited," Journal of Population Economics, Springer;European Society for Population Economics, vol. 38(3), pages 1-41, September.
    7. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
    8. Goller, Daniel & Lechner, Michael & Moczall, Andreas & Wolff, Joachim, 2019. "Does the estimation of the propensity score by machine learning improve matching estimation? The case of Germany’s programmes for long term unemployed," Economics Working Paper Series 1910, University of St. Gallen, School of Economics and Political Science.
    9. Kevin Credit & Matthew Lehnert, 2024. "A structured comparison of causal machine learning methods to assess heterogeneous treatment effects in spatial data," Journal of Geographical Systems, Springer, vol. 26(4), pages 483-510, October.
    10. 'Agoston Reguly, 2021. "Discovering Heterogeneous Treatment Effects in Regression Discontinuity Designs," Papers 2106.11640, arXiv.org, revised Aug 2025.
    11. Michael Lechner & Jana Mareckova, 2024. "Comprehensive Causal Machine Learning," Papers 2405.10198, arXiv.org, revised Feb 2025.
    12. Olga Takács & János Vincze, 2023. "Heterogeneous wage structure effects: a partial European East-West comparison," KRTK-KTI WORKING PAPERS 2305, Institute of Economics, Centre for Economic and Regional Studies.
    13. Kleifgen, Eva & Lang, Julia, 2022. "Should I Train Or Should I Go? Estimating Treatment Effects of Retraining on Regional and Occupational Mobility," VfS Annual Conference 2022 (Basel): Big Data in Economics 264069, Verein für Socialpolitik / German Economic Association.
    14. Xinru WANG & Nina DELIU & Yusuke NARITA & Bibhas CHAKRABORTY, 2023. "SMART-EXAM: Incorporating Participants' Welfare into Sequential Multiple Assignment Randomized Trials," Discussion papers 23081, Research Institute of Economy, Trade and Industry (RIETI).
    15. Jacob, Daniel, 2020. "Cross-Fitting and Averaging for Machine Learning Estimation of Heterogeneous Treatment Effects," IRTG 1792 Discussion Papers 2020-014, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    16. Daniel Goller & Tamara Harrer & Michael Lechner & Joachim Wolff, 2021. "Active labour market policies for the long-term unemployed: New evidence from causal machine learning," Papers 2106.10141, arXiv.org, revised May 2023.
    17. Climent Quintana-Domeque & Jingya Zeng, 2023. "COVID-19 and mental health: natural experiments of the costs of lockdowns," Discussion Papers 2314, University of Exeter, Department of Economics.
    18. Knaus, Michael C., 2020. "Double Machine Learning Based Program Evaluation under Unconfoundedness," IZA Discussion Papers 13051, IZA Network @ LISER.
    19. Fabian Muny, 2025. "Evaluating Program Sequences with Double Machine Learning: An Application to Labor Market Policies," Papers 2506.11960, arXiv.org.
    20. Dana Turjeman & Fred M. Feinberg, 2024. "When the Data Are Out: Measuring Behavioral Changes Following a Data Breach," Marketing Science, INFORMS, vol. 43(2), pages 440-461, March.
    21. Achim Ahrens & Alessandra Stampi-Bombelli & Selina Kurer & Dominik Hangartner, 2023. "Optimal multi-action treatment allocation: A two-phase field experiment to boost immigrant naturalization," Papers 2305.00545, arXiv.org, revised Feb 2024.
    22. Daniel Jacob, 2021. "CATE meets ML -- The Conditional Average Treatment Effect and Machine Learning," Papers 2104.09935, arXiv.org, revised Apr 2021.
    23. Pons Rotger, Gabriel & Rosholm, Michael, 2020. "The Role of Beliefs in Long Sickness Absence: Experimental Evidence from a Psychological Intervention," IZA Discussion Papers 13582, IZA Network @ LISER.
    24. Axenbeck, Janna & Berner, Anne & Kneib, Thomas, 2022. "What drives the relationship between digitalization and industrial energy demand? Exploring firm-level heterogeneity," ZEW Discussion Papers 22-059, ZEW - Leibniz Centre for European Economic Research.
    25. Daniel Goller, 2023. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Annals of Operations Research, Springer, vol. 325(1), pages 649-679, June.
    26. Nora Bearth & Michael Lechner, 2024. "Causal Machine Learning for Moderation Effects," Papers 2401.08290, arXiv.org, revised Jan 2025.
    27. Robin M. Gubela & Stefan Lessmann & Björn Stöcker, 2024. "Multiple Treatment Modeling for Target Marketing Campaigns: A Large-Scale Benchmark Study," Information Systems Frontiers, Springer, vol. 26(3), pages 875-898, June.
    28. Sushant More & Priya Kotwal & Sujith Chappidi & Dinesh Mandalapu & Chris Khawand, 2024. "Double Machine Learning at Scale to Predict Causal Impact of Customer Actions," Papers 2409.02332, arXiv.org.
    29. Denis Fougère & Nicolas Jacquemet, 2021. "Policy Evaluation Using Causal Inference Methods," PSE-Ecole d'économie de Paris (Postprint) hal-03098058, HAL.
    30. David Rey-Blanco & Pelayo Arbués & Fernando A. López & Antonio Páez, 2024. "Using machine learning to identify spatial market segments. A reproducible study of major Spanish markets," Environment and Planning B, , vol. 51(1), pages 89-108, January.
    31. Silvia Coderoni & Roberto Esposti & Alessandro Varacca, 2024. "How Differently Do Farms Respond to Agri-environmental Policies? A Probabilistic Machine-Learning Approach," Land Economics, University of Wisconsin Press, vol. 100(2), pages 370-397.
    32. Boller, Daniel & Lechner, Michael & Okasa, Gabriel, 2021. "The Effect of Sport in Online Dating: Evidence from Causal Machine Learning," IZA Discussion Papers 14259, IZA Network @ LISER.
    33. Yiyi Huo & Yingying Fan & Fang Han, 2023. "On the adaptation of causal forests to manifold data," Papers 2311.16486, arXiv.org, revised Dec 2023.
    34. Elena Denisova-Schmidt & Martin Huber & Elvira Leontyeva & Anna Solovyeva, 2021. "Combining experimental evidence with machine learning to assess anti-corruption educational campaigns among Russian university students," Empirical Economics, Springer, vol. 60(4), pages 1661-1684, April.
    35. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
    36. Zhuan Pei, 2024. "Supercompliers," Economics Virtual Symposium 2024 06, Stata Users Group.
      • Matthew L. Comey & Amanda R. Eng & Pauline Leung & Zhuan Pei, 2022. "Supercompliers," Papers 2212.14105, arXiv.org, revised Dec 2024.
    37. Daniele Ballinari & Nora Bearth, 2024. "Improving the Finite Sample Estimation of Average Treatment Effects using Double/Debiased Machine Learning with Propensity Score Calibration," Papers 2409.04874, arXiv.org, revised Jan 2025.
    38. Haupt, Johannes & Lessmann, Stefan, 2022. "Targeting customers under response-dependent costs," European Journal of Operational Research, Elsevier, vol. 297(1), pages 369-379.
    39. Santiago Carbo-Valverde & Pedro Cuadros-Solas & Francisco Rodríguez-Fernández, 2020. "A machine learning approach to the digitalization of bank customers: Evidence from random and causal forests," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-39, October.
    40. Ben-Nasr, Hamdi & Masry, Shadin & Masum, Abdullah-Al & Ouni, Zeineb, 2025. "Carbon risk and trade credit," International Review of Economics & Finance, Elsevier, vol. 103(C).
    41. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
    42. Erik Engberg & Holger Gorg & Magnus Lodefalk & Farrukh Javed & Martin Langkvist & Natalia Monteiro & Hildegunn Nordas & Giuseppe Pulito & Sarah Schroeder & Aili Tang, 2024. "AI Unboxed and Jobs: A Novel Measure and Firm-Level Evidence from Three Countries," RFBerlin Discussion Paper Series 2414, ROCKWOOL Foundation Berlin (RFBerlin).
    43. Riccardo Di Francesco, 2022. "Aggregation Trees," CEIS Research Paper 546, Tor Vergata University, CEIS, revised 20 Nov 2023.
    44. Gert Bijnens & Shyngys Karimov & Jozef Konings, 2023. "Does Automatic Wage Indexation Destroy Jobs? A Machine Learning Approach," De Economist, Springer, vol. 171(1), pages 85-117, March.
    45. Joshua Angrist & Brigham Frandsen, 2019. "Machine Labor," NBER Working Papers 26584, National Bureau of Economic Research, Inc.
    46. Susan Athey & Guido W. Imbens & Jonas Metzger & Evan M. Munro, 2019. "Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations," NBER Working Papers 26566, National Bureau of Economic Research, Inc.
    47. Haupt, Johannes & Lessmann, Stefan, 2020. "Targeting Cutsomers Under Response-Dependent Costs," IRTG 1792 Discussion Papers 2020-005, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    48. Phillip Heiler & Michael C. Knaus, 2021. "Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments," Papers 2110.01427, arXiv.org, revised Aug 2023.
    49. Michael Zimmert & Michael Lechner, 2019. "Nonparametric estimation of causal heterogeneity under high-dimensional confounding," Papers 1908.08779, arXiv.org.
    50. Haupt, Johannes & Jacob, Daniel & Gubela, Robin M. & Lessmann, Stefan, 2019. "Affordable Uplift: Supervised Randomization in Controlled Exprtiments," IRTG 1792 Discussion Papers 2019-026, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    51. Daniel Jacob, 2019. "Group Average Treatment Effects for Observational Studies," Papers 1911.02688, arXiv.org, revised Mar 2020.
    52. Michael Lechner & Jana Mareckova, 2022. "Modified Causal Forest," Papers 2209.03744, arXiv.org.
    53. Federica Mascolo & Nora Bearth & Fabian Muny & Michael Lechner & Jana Mareckova, 2024. "From Average Effects to Targeted Assignment: A Causal Machine Learning Analysis of Swiss Active Labor Market Policies," Papers 2410.23322, arXiv.org, revised May 2025.
    54. Roberto Esposti, 2022. "Non-Monetary Motivations Of Agroenvironmental Policies Adoption. A Causal Forest Approach," Working Papers 459, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    55. Vishalie Shah & Julia Hatamyar & Taufik Hidayat & Noemi Kreif, 2025. "Exploring the heterogeneous impacts of Indonesia's conditional cash transfer scheme (PKH) on maternal health care utilisation using instrumental causal forests," Papers 2501.12803, arXiv.org.
    56. Ogundari, Kolawole, 2021. "A systematic review of statistical methods for estimating an education production function," MPRA Paper 105283, University Library of Munich, Germany.
    57. Michael Lechner & Gabriel Okasa, 2025. "Random Forest estimation of the ordered choice model," Empirical Economics, Springer, vol. 68(1), pages 1-106, January.
    58. Riccardo Di Francesco, 2024. "Aggregation Trees," Papers 2410.11408, arXiv.org, revised Oct 2025.
    59. Johannes Haupt & Stefan Lessmann, 2020. "Targeting customers under response-dependent costs," Papers 2003.06271, arXiv.org, revised Aug 2021.
    60. Burlat, Héloïse, 2024. "Everybody’s got to learn sometime? A causal machine learning evaluation of training programmes for jobseekers in France," Labour Economics, Elsevier, vol. 89(C).
    61. Roberto Esposti, 2022. "The Coevolution of Policy Support and Farmers' Behaviour. An investigation on Italian agriculture over the 2008-2019 period," Working Papers 464, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    62. Patrick Rehill & Nicholas Biddle, 2025. "Policy Learning for Many Outcomes of Interest: Combining Optimal Policy Trees with Multi-objective Bayesian Optimisation," Computational Economics, Springer;Society for Computational Economics, vol. 66(2), pages 971-1001, August.
    63. Elliott Ash & Sergio Galletta & Tommaso Giommoni, 2021. "A Machine Learning Approach to Analyze and Support Anti-Corruption Policy," CESifo Working Paper Series 9015, CESifo.
    64. Patrick Rehill & Nicholas Biddle, 2022. "Policy learning for many outcomes of interest: Combining optimal policy trees with multi-objective Bayesian optimisation," Papers 2212.06312, arXiv.org, revised Oct 2023.
    65. Evan D. Peet & Dana Schultz & Susan Lovejoy & Fuchiang (Rich) Tsui, 2023. "Variation in the infant health effects of the women, infants, and children program by predicted risk using novel machine learning methods," Health Economics, John Wiley & Sons, Ltd., vol. 32(1), pages 194-217, January.
    66. Rafael Quintana, 2023. "Embracing complexity in social science research," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(1), pages 15-38, February.
    67. Gabriel Okasa, 2022. "Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance," Papers 2201.12692, arXiv.org.
    68. Olga Takács & János Vincze, 2023. "Where is the pain the most acute? The market segments particularly affected by gender wage discrimination in Hungary," KRTK-KTI WORKING PAPERS 2304, Institute of Economics, Centre for Economic and Regional Studies.
    69. Evan D. Peet & Dana Schultz & Susan Lovejoy & Fuchiang (Rich) Tsui, 2024. "The infant health effects of doulas: Leveraging big data and machine learning to inform cost‐effective targeting," Health Economics, John Wiley & Sons, Ltd., vol. 33(6), pages 1387-1411, June.
    70. Strittmatter, Anthony, 2023. "What is the value added by using causal machine learning methods in a welfare experiment evaluation?," Labour Economics, Elsevier, vol. 84(C).
    71. Finn Lattimore & Daniel M. Steinberg & Anna Zhu, 2023. "The Economic Effect of Gaining a New Qualification Later in Life," Papers 2304.01490, arXiv.org, revised Apr 2023.
    72. Verbeke, Wouter & Olaya, Diego & Guerry, Marie-Anne & Van Belle, Jente, 2023. "To do or not to do? Cost-sensitive causal classification with individual treatment effect estimates," European Journal of Operational Research, Elsevier, vol. 305(2), pages 838-852.

  9. Michael Knaus & Michael Lechner & Anthony Strittmatter, 2017. "Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach," Papers 1709.10279, arXiv.org, revised May 2018.

    Cited by:

    1. Lechner, Michael, 2019. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," CEPR Discussion Papers 13430, C.E.P.R. Discussion Papers.
    2. Anna Baiardi & Andrea A. Naghi, 2021. "The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies," Tinbergen Institute Discussion Papers 21-001/V, Tinbergen Institute.
    3. Lechner, Michael & Knaus, Michael C. & Strittmatter, Anthony, 2018. "Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence," CEPR Discussion Papers 13402, C.E.P.R. Discussion Papers.
    4. 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.
    5. Bart Cockx & Michael Lechner & Joost Bollens, 2020. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," LIDAM Discussion Papers IRES 2020016, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
    6. Michael Lechner & Jana Mareckova, 2024. "Comprehensive Causal Machine Learning," Papers 2405.10198, arXiv.org, revised Feb 2025.
    7. Buhl-Wiggers, Julie & Kerwin, Jason & Muñoz-Morales, Juan S. & Smith, Jeffrey A. & Thornton, Rebecca L., 2020. "Some Children Left Behind: Variation in the Effects of an Educational Intervention," IZA Discussion Papers 13598, IZA Network @ LISER.
    8. Paul S. Clarke & Annalivia Polselli, 2023. "Double Machine Learning for Static Panel Models with Fixed Effects," Papers 2312.08174, arXiv.org, revised Dec 2024.
    9. Olga Takács & János Vincze, 2023. "Heterogeneous wage structure effects: a partial European East-West comparison," KRTK-KTI WORKING PAPERS 2305, Institute of Economics, Centre for Economic and Regional Studies.
    10. Ulrike Unterhofer & Conny Wunsch, 2022. "Macroeconomic Effects of Active Labour Market Policies: A Novel Instrumental Variables Approach," Papers 2211.12437, arXiv.org.
    11. Daniel Goller & Tamara Harrer & Michael Lechner & Joachim Wolff, 2021. "Active labour market policies for the long-term unemployed: New evidence from causal machine learning," Papers 2106.10141, arXiv.org, revised May 2023.
    12. Bonev, Petyo, 2020. "Nonparametric identification in nonseparable duration models with unobserved heterogeneity," Economics Working Paper Series 2005, University of St. Gallen, School of Economics and Political Science.
    13. Knaus, Michael C., 2020. "Double Machine Learning Based Program Evaluation under Unconfoundedness," IZA Discussion Papers 13051, IZA Network @ LISER.
    14. Ballinari, Daniele, 2024. "Calibrating doubly-robust estimators with unbalanced treatment assignment," Economics Letters, Elsevier, vol. 241(C).
    15. Miller, Steve, 2020. "Causal forest estimation of heterogeneous and time-varying environmental policy effects," Journal of Environmental Economics and Management, Elsevier, vol. 103(C).
    16. Doerr, Annabelle, 2022. "Vocational Training for Female Job Returners - Effects on Employment, Earnings and Job Quality," Working papers 2022/02, Faculty of Business and Economics - University of Basel.
    17. Denis Fougère & Nicolas Jacquemet, 2019. "Causal Inference and Impact Evaluation," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 510-511-5, pages 181-200.
    18. Nora Bearth & Michael Lechner, 2024. "Causal Machine Learning for Moderation Effects," Papers 2401.08290, arXiv.org, revised Jan 2025.
    19. Anna Baiardi & Andrea A. Naghi, 2021. "The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies," Papers 2101.00878, arXiv.org.
    20. Nora Bearth & Michael Lechner & Jana Mareckova & Fabian Muny, 2025. "Fairness-Aware and Interpretable Policy Learning," Papers 2509.12119, arXiv.org.
    21. Pamela Giustinelli & Matthew D. Shapiro, 2019. "SeaTE: Subjective ex ante Treatment Effect of Health on Retirement," NBER Working Papers 26087, National Bureau of Economic Research, Inc.
    22. Strittmatter, Anthony, 2019. "What is the Value Added by using Causal Machine Learning Methods in a Welfare Experiment Evaluation?," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203499, Verein für Socialpolitik / German Economic Association.
    23. Pytka, Krzysztof & Gulyas, Andreas, 2021. "Understanding the Sources of Earnings Losses After Job Displacement: A Machine-Learning Approach," VfS Annual Conference 2021 (Virtual Conference): Climate Economics 242402, Verein für Socialpolitik / German Economic Association.
    24. Anna Baiardi & Andrea A Naghi, 2024. "The value added of machine learning to causal inference: evidence from revisited studies," The Econometrics Journal, Royal Economic Society, vol. 27(2), pages 213-234.
    25. Elena Denisova-Schmidt & Martin Huber & Elvira Leontyeva & Anna Solovyeva, 2021. "Combining experimental evidence with machine learning to assess anti-corruption educational campaigns among Russian university students," Empirical Economics, Springer, vol. 60(4), pages 1661-1684, April.
    26. Daniele Ballinari & Nora Bearth, 2024. "Improving the Finite Sample Estimation of Average Treatment Effects using Double/Debiased Machine Learning with Propensity Score Calibration," Papers 2409.04874, arXiv.org, revised Jan 2025.
    27. Doerr, Annabelle, 2022. "Vocational training for female job returners - Effects on employment, earnings and job quality," Labour Economics, Elsevier, vol. 75(C).
    28. Denis Fougère & Nicolas Jacquemet, 2021. "Policy Evaluation Using Causal Inference Methods," Post-Print hal-03098058, HAL.
    29. Dario Sansone & Anna Zhu, 2020. "Using Machine Learning to Create an Early Warning System for Welfare Recipients," Papers 2011.12057, arXiv.org, revised May 2021.
    30. Michael Lechner & Jana Mareckova, 2022. "Modified Causal Forest," Papers 2209.03744, arXiv.org.
    31. Federica Mascolo & Nora Bearth & Fabian Muny & Michael Lechner & Jana Mareckova, 2024. "From Average Effects to Targeted Assignment: A Causal Machine Learning Analysis of Swiss Active Labor Market Policies," Papers 2410.23322, arXiv.org, revised May 2025.
    32. Athey, Susan & Keleher, Niall & Spiess, Jann, 2025. "Machine learning who to nudge: Causal vs predictive targeting in a field experiment on student financial aid renewal," Journal of Econometrics, Elsevier, vol. 249(PC).
    33. Achim Ahrens & Alessandra Stampi‐Bombelli & Selina Kurer & Dominik Hangartner, 2024. "Optimal multi‐action treatment allocation: A two‐phase field experiment to boost immigrant naturalization," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(7), pages 1379-1395, November.
    34. Tomoko Nagai & Takayuki Okuda & Tomoya Nakamura & Yuichiro Sato & Yusuke Sato & Kensaku Kinjo & Kengo Kawamura & Shin Kikuta & Naoto Kumano-go, 2024. "Educational Effects in Mathematics: Conditional Average Treatment Effect depending on the Number of Treatments," Papers 2411.01498, arXiv.org.
    35. 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.
    36. Strittmatter, Anthony, 2023. "What is the value added by using causal machine learning methods in a welfare experiment evaluation?," Labour Economics, Elsevier, vol. 84(C).
    37. Denzler, Stefan & Ruhose, Jens & Wolter, Stefan C., 2025. "Labour market effects of work-related continuous education in Switzerland – evidence from administrative data," Economics of Education Review, Elsevier, vol. 107(C).
    38. Finn Lattimore & Daniel M. Steinberg & Anna Zhu, 2023. "The Economic Effect of Gaining a New Qualification Later in Life," Papers 2304.01490, arXiv.org, revised Apr 2023.

  10. Michael C. Knaus & Steffen Otterbach, 2016. "Work Hour Mismatch and Job Mobility: Adjustment Channels and Resolution Rates," SOEPpapers on Multidisciplinary Panel Data Research 825, DIW Berlin, The German Socio-Economic Panel (SOEP).

    Cited by:

    1. Fischer, Benjamin & Jessen, Robin & Steiner, Viktor, 2019. "Work incentives and the efficiency of tax-transfer reforms under constrained labor supply," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203607, Verein für Socialpolitik / German Economic Association.
    2. Weber, Enzo & Zimmert, Franziska, 2017. "The creation and resolution of working hour discrepancies over the life course," IAB-Discussion Paper 201729, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    3. Mattis Beckmannshagen & Rick Glaubitz, 2023. "Is There a Desired Added Worker Effect?: Evidence from Involuntary Job Losses," SOEPpapers on Multidisciplinary Panel Data Research 1200, DIW Berlin, The German Socio-Economic Panel (SOEP).
    4. Theresa Markefke & Rebekka Rehm, 2020. "Macroeconomic Determinants of Involuntary Part-Time Employment in Germany," Working Paper Series in Economics 103, University of Cologne, Department of Economics.
    5. Fischer, Benjamin & Jessen, Robin & Steiner, Viktor, 2019. "Work incentives and the cost of redistribution via tax-transfer reforms under constrained labor supply," Discussion Papers 2019/10, Free University Berlin, School of Business & Economics.
    6. Irina Frei & Christian Grund, 2022. "Working-time mismatch and job satisfaction of junior academics," Journal of Business Economics, Springer, vol. 92(7), pages 1125-1166, September.
    7. Christian Grund & Katja Rebecca Tilkes, 2021. "Working Time Mismatch and Job Satisfaction: The Role of Employees’ Time Autonomy and Gender," SOEPpapers on Multidisciplinary Panel Data Research 1149, DIW Berlin, The German Socio-Economic Panel (SOEP).
    8. Alameddine, Mohamad & Otterbach, Steffen & Rafii, Bayan & Sousa-Poza, Alfonso, 2018. "Work hour constraints in the German nursing workforce: A quarter of a century in review," Health Policy, Elsevier, vol. 122(10), pages 1101-1108.
    9. Miklós ANTAL & Benedikt LEHMANN & Thiago GUIMARAES & Alexandra HALMOS & Bence LUKÁCS, 2024. "Shorter hours wanted? A systematic review of working‐time preferences and outcomes," International Labour Review, International Labour Organization, vol. 163(1), pages 25-47, March.
    10. Wanger, Susanne, 2017. "What makes employees satisfied with their working time? : The role of working hours, time-sovereignty and working conditions for working time and job satisfaction," IAB-Discussion Paper 201720, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].

Articles

  1. Michael C. Knaus & Michael Lechner & Anthony Strittmatter, 2022. "Heterogeneous Employment Effects of Job Search Programs: A Machine Learning Approach," Journal of Human Resources, University of Wisconsin Press, vol. 57(2), pages 597-636.
    See citations under working paper version above.
  2. Michael C Knaus, 2022. "Double machine learning-based programme evaluation under unconfoundedness [Econometric methods for program evaluation]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 602-627.
    See citations under working paper version above.
  3. Michael C. Knaus, 2021. "A double machine learning approach to estimate the effects of musical practice on student’s skills," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 282-300, January.
    See citations under working paper version above.
  4. Michael C Knaus & Michael Lechner & Anthony Strittmatter, 2021. "Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence," The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 134-161.
    See citations under working paper version above.
  5. Knaus, Michael C. & Lechner, Michael & Reimers, Anne K., 2020. "For better or worse? – The effects of physical education on child development," Labour Economics, Elsevier, vol. 67(C).
    See citations under working paper version above.
  6. Michael C. Knaus & Steffen Otterbach, 2019. "Work Hour Mismatch And Job Mobility: Adjustment Channels And Resolution Rates," Economic Inquiry, Western Economic Association International, vol. 57(1), pages 227-242, January.
    See citations under working paper version above.Sorry, no citations of articles recorded.

Chapters

  1. Daniel Goller & Michael C. Knaus & Michael Lechner & Gabriel Okasa, 2021. "Predicting match outcomes in football by an Ordered Forest estimator," Chapters, in: Ruud H. Koning & Stefan Kesenne (ed.), A Modern Guide to Sports Economics, chapter 22, pages 335-355, Edward Elgar Publishing.
    See citations under working paper version above.Sorry, no citations of chapters recorded.
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