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Double/Debiased Machine Learning for Treatment and Structural Parameters
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Cited by:
- Ying Liu & Haoran Zhao & Jieguang Sun & Yahui Tang, 2022. "Digital Inclusive Finance and Family Wealth: Evidence from LightGBM Approach," Sustainability, MDPI, vol. 14(22), pages 1-19, November.
- Guanhao Feng & Stefano Giglio & Dacheng Xiu, 2020.
"Taming the Factor Zoo: A Test of New Factors,"
Journal of Finance, American Finance Association, vol. 75(3), pages 1327-1370, June.
- Guanhao Feng & Stefano Giglio & Dacheng Xiu, 2019. "Taming the Factor Zoo: A Test of New Factors," NBER Working Papers 25481, National Bureau of Economic Research, Inc.
- Giglio, Stefano & Feng, Guanhao & Xiu, Dacheng, 2020. "Taming the Factor Zoo: A Test of New Factors," CEPR Discussion Papers 14266, C.E.P.R. Discussion Papers.
- Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018.
"High-dimensional econometrics and regularized GMM,"
CeMMAP working papers
CWP35/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.
- 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.
- Goller, Daniel, 2020. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Economics Working Paper Series 2013, University of St. Gallen, School of Economics and Political Science.
- Daniel Goller, 2020. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Papers 2008.07165, arXiv.org.
- Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Sep 2023.
- Iván Díaz & Nima S. Hejazi, 2020. "Causal mediation analysis for stochastic interventions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 661-683, July.
- Jiafeng Chen & David M. Ritzwoller, 2021. "Semiparametric Estimation of Long-Term Treatment Effects," Papers 2107.14405, arXiv.org, revised Aug 2023.
- MIYAKAWA Daisuke, 2019. "Shocks to Supply Chain Networks and Firm Dynamics: An Application of Double Machine Learning," Discussion papers 19100, Research Institute of Economy, Trade and Industry (RIETI).
- Nicolaj N. Mühlbach, 2020. "Tree-based Synthetic Control Methods: Consequences of moving the US Embassy," CREATES Research Papers 2020-04, Department of Economics and Business Economics, Aarhus University.
- Delprato, Marcos & Frola, Alessia & Antequera, Germán, 2022. "Indigenous and non-Indigenous proficiency gaps for out-of-school and in-school populations: A machine learning approach," International Journal of Educational Development, Elsevier, vol. 93(C).
- Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP72/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Julián Caballero & Christian Upper, 2023. "What happens to EMEs when US yields go up?," BIS Working Papers 1081, Bank for International Settlements.
- Tang, Shengfang & Huang, Zhilin, 2022. "Empirical likelihood confidence interval for difference-in-differences estimator with panel data," Economics Letters, Elsevier, vol. 216(C).
- Aysegül Kayaoglu & Ghassan Baliki & Tilman Brück & Melodie Al Daccache & Dorothee Weiffen, 2023. "How to conduct impact evaluations in humanitarian and conflict settings," HiCN Working Papers 387, Households in Conflict Network.
- Neng-Chieh Chang, 2020. "The Mode Treatment Effect," Papers 2007.11606, arXiv.org.
- Michael Bailey & Drew Johnston & Theresa Kuchler & Johannes Stroebel & Arlene Wong, 2022.
"Peer Effects in Product Adoption,"
American Economic Journal: Applied Economics, American Economic Association, vol. 14(3), pages 488-526, July.
- Theresa Kuchler & Arlene Wong & Johannes Stroebel, 2018. "Peer effects in product adoption," 2018 Meeting Papers 1001, Society for Economic Dynamics.
- Michael Bailey & Drew Johnston & Theresa Kuchler & Johannes Stroebel & Arlene Wong, 2019. "Peer effects in product adoption," CESifo Working Paper Series 7685, CESifo.
- Ströbel, Johannes & Bailey, Michael & Johnston, Drew & Kuchler, Theresa & Wong, Arlene, 2019. "Peer Effects in Product Adoption," CEPR Discussion Papers 13731, C.E.P.R. Discussion Papers.
- Michael Bailey & Drew M. Johnston & Theresa Kuchler & Johannes Stroebel & Arlene Wong, 2019. "Peer Effects in Product Adoption," NBER Working Papers 25843, National Bureau of Economic Research, Inc.
- Michael Bailey & Drew Johnston & Theresa Kuchler & Johannes Stroebel & Arlene Wong, 2021. "Peer Effects in Product Adoption," Working Papers 2021-66, Princeton University. Economics Department..
- Sant’Anna, Pedro H.C. & Zhao, Jun, 2020.
"Doubly robust difference-in-differences estimators,"
Journal of Econometrics, Elsevier, vol. 219(1), pages 101-122.
- Pedro H. C. Sant'Anna & Jun B. Zhao, 2018. "Doubly Robust Difference-in-Differences Estimators," Papers 1812.01723, arXiv.org, revised May 2020.
- Harsh Parikh & Carlos Varjao & Louise Xu & Eric Tchetgen Tchetgen, 2022. "Validating Causal Inference Methods," Papers 2202.04208, arXiv.org, revised Jul 2022.
- Kaspar Wuthrich & Ying Zhu, 2019. "Omitted variable bias of Lasso-based inference methods: A finite sample analysis," Papers 1903.08704, arXiv.org, revised Sep 2021.
- Ruoxuan Xiong & Allison Koenecke & Michael Powell & Zhu Shen & Joshua T. Vogelstein & Susan Athey, 2021.
"Federated Causal Inference in Heterogeneous Observational Data,"
Papers
2107.11732, arXiv.org, revised Apr 2023.
- Xiong, Ruoxuan & Koenecke, Allison & Powell, Michael & Shen, Zhu & Vogelstein, Joshua T. & Athey, Susan, 2021. "Federated Causal Inference in Heterogeneous Observational Data," Research Papers 3990, Stanford University, Graduate School of Business.
- Martin Huber, 2019.
"An introduction to flexible methods for policy evaluation,"
Papers
1910.00641, arXiv.org.
- 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.
- Elyasiani, Elyas & Movaghari, Hadi, 2022. "Determinants of corporate cash holdings: An application of a robust variable selection technique," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 967-993.
- Patrick M. Schnell & Richard Baumgartner & Shahrul Mt‐Isa & Vladimir Svetnik, 2022. "A principal stratification approach to estimating the effect of continuing treatment after observing early outcomes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1065-1084, November.
- Jason Poulos & Shuxi Zeng, 2021. "RNN‐based counterfactual prediction, with an application to homestead policy and public schooling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 1124-1139, August.
- Victor Chernozhukov & Mert Demirer & Esther Duflo & Ivan Fernandez-Val, 2017.
"Generic machine learning inference on heterogenous treatment effects in randomized experiments,"
CeMMAP working papers
61/17, Institute for Fiscal Studies.
- Victor Chernozhukov & Mert Demirer & Esther Duflo & Ivan Fernandez-Val, 2017. "Generic machine learning inference on heterogenous treatment effects in randomized experiments," CeMMAP working papers CWP61/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Vira Semenova & Matt Goldman & Victor Chernozhukov & Matt Taddy, 2023. "Inference on heterogeneous treatment effects in high‐dimensional dynamic panels under weak dependence," Quantitative Economics, Econometric Society, vol. 14(2), pages 471-510, May.
- Susan Athey & Guido W. Imbens & Stefan Wager, 2018.
"Approximate residual balancing: debiased inference of average treatment effects in high dimensions,"
Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(4), pages 597-623, September.
- Susan Athey & Guido W. Imbens & Stefan Wager, 2016. "Approximate Residual Balancing: De-Biased Inference of Average Treatment Effects in High Dimensions," Papers 1604.07125, arXiv.org, revised Jan 2018.
- Jiaming Zeng & Michael F. Gensheimer & Daniel L. Rubin & Susan Athey & Ross D. Shachter, 2022.
"Uncovering interpretable potential confounders in electronic medical records,"
Nature Communications, Nature, vol. 13(1), pages 1-14, December.
- Zeng, Jiaming & Gensheimer, Michael F. & Rubin, Daniel L. & Athey, Susan & Schachter, Ross D., 2021. "Uncovering Interpretable Potential Confounders in Electronic Medical Records," Research Papers 3950, Stanford University, Graduate School of Business.
- 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.
- Jelena Bradic & Weijie Ji & Yuqian Zhang, 2021. "High-dimensional Inference for Dynamic Treatment Effects," Papers 2110.04924, arXiv.org, revised May 2023.
- Andrew B. Martinez, 2020.
"Forecast Accuracy Matters for Hurricane Damage,"
Econometrics, MDPI, vol. 8(2), pages 1-24, May.
- Andrew B. Martinez, 2020. "Forecast Accuracy Matters for Hurricane Damages," Working Papers 2020-003, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
- Achim Ahrens & Christian B. Hansen & Mark E. Schaffer & Thomas Wiemann, 2024.
"ddml: Double/debiased machine learning in Stata,"
Stata Journal, StataCorp LP, vol. 24(1), pages 3-45, March.
- Christian B. Hansen & Mark E. Schaffer & Thomas Wiemann & Achim Ahrens, 2022. "ddml: Double/debiased machine learning in Stata," Swiss Stata Conference 2022 02, Stata Users Group.
- Ahrens, Achim & Hansen, Christian B. & Schaffer, Mark E & Wiemann, Thomas, 2023. "ddml: Double/Debiased Machine Learning in Stata," IZA Discussion Papers 15963, Institute of Labor Economics (IZA).
- Achim Ahrens & Christian B. Hansen & Mark E. Schaffer & Thomas Wiemann, 2023. "ddml: Double/debiased machine learning in Stata," Papers 2301.09397, arXiv.org, revised Jan 2024.
- Qingliang Fan & Zijian Guo & Ziwei Mei & Cun-Hui Zhang, 2023. "Uniform Inference for Nonlinear Endogenous Treatment Effects with High-Dimensional Covariates," Papers 2310.08063, arXiv.org, revised Oct 2023.
- Aristide Houndetoungan & Abdoul Haki Maoude, 2024. "Inference for Two-Stage Extremum Estimators," Papers 2402.05030, arXiv.org.
- Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
- Goller, Daniel & Lechner, Michael & Moczall, Andreas & Wolff, Joachim, 2020.
"Does the estimation of the propensity score by machine learning improve matching estimation? The case of Germany's programmes for long term unemployed,"
Labour Economics, Elsevier, vol. 65(C).
- 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," IZA Discussion Papers 12526, Institute of Labor Economics (IZA).
- 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.
- Goller, Daniel & Lechner, Michael & Moczall, Andreas & Wolff, Joachim, 2020. "Does the estimation of the propensity score by machine learning improve matching estimation? : The case of Germany's programmes for long term unemployed," IAB-Discussion Paper 202005, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
- 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.
- Knaus, Michael C. & Lechner, Michael & Strittmatter, Anthony, 2018. "Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence," IZA Discussion Papers 12039, Institute of Labor Economics (IZA).
- 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.
- Knaus, Michael C. & Lechner, Michael & anthony.strittmatter@unisg.ch, 2018. "Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence," Economics Working Paper Series 1817, University of St. Gallen, School of Economics and Political Science.
- 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.
- Merlin Stein, 2022. "When are large female-led firms more resilient against shocks? Learnings from Indian enterprises during COVID-19 with diff-in-diff and causal forests," CSAE Working Paper Series 2022-01, Centre for the Study of African Economies, University of Oxford.
- Christiansen, T. & Weeks, M., 2020. "Distributional Aspects of Microcredit Expansions," Cambridge Working Papers in Economics 20100, Faculty of Economics, University of Cambridge.
- Yuya Sasaki & Takuya Ura & Yichong Zhang, 2022.
"Unconditional quantile regression with high‐dimensional data,"
Quantitative Economics, Econometric Society, vol. 13(3), pages 955-978, July.
- Yuya Sasaki & Takuya Ura & Yichong Zhang, 2020. "Unconditional Quantile Regression with High Dimensional Data," Papers 2007.13659, arXiv.org, revised Feb 2022.
- 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.
- Maximilian Maurice Gail & Phil-Adrian Klotz, 2021. "The Impact of the Agency Model on E-book Prices: Evidence from the UK," MAGKS Papers on Economics 202111, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
- AmirEmad Ghassami & Andrew Ying & Ilya Shpitser & Eric Tchetgen Tchetgen, 2021. "Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inference," Papers 2104.02929, arXiv.org, revised Mar 2022.
- Nadja van 't Hoff, 2023. "Identifying Causal Effects of Nonbinary, Ordered Treatments using Multiple Instrumental Variables," Papers 2311.17575, arXiv.org.
- Pedro Carneiro & Sokbae Lee & Daniel Wilhelm, 2020.
"Optimal data collection for randomized control trials [Microcredit impacts: Evidence from a randomized microcredit program placement experiment by Compartamos Banco],"
The Econometrics Journal, Royal Economic Society, vol. 23(1), pages 1-31.
- Pedro Carneiro & Sokbae (Simon) Lee & Daniel Wilhelm, 2016. "Optimal data collection for randomized control trials," CeMMAP working papers CWP15/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Pedro Carneiro & Sokbae (Simon) Lee & Daniel Wilhelm, 2017. "Optimal data collection for randomized control trials," CeMMAP working papers CWP15/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Pedro Carneiro & Sokbae (Simon) Lee & Daniel Wilhelm, 2019. "Optimal Data Collection for Randomized Control Trials," CeMMAP working papers CWP21/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Carneiro, Pedro & Lee, Sokbae & Wilhelm, Daniel, 2016. "Optimal Data Collection for Randomized Control Trials," IZA Discussion Papers 9908, Institute of Labor Economics (IZA).
- Pedro Carneiro & Sokbae Lee & Daniel Wilhelm, 2016. "Optimal Data Collection for Randomized Control Trials," Papers 1603.03675, arXiv.org, revised Aug 2016.
- Pedro Carneiro & Sokbae (Simon) Lee & Daniel Wilhelm, 2017. "Optimal data collection for randomized control trials," CeMMAP working papers CWP45/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Daniel Jacob, 2021. "CATE meets ML," Digital Finance, Springer, vol. 3(2), pages 99-148, June.
- Gehrke, Esther & Lenel, Friederike & Schupp, Claudia, 2022. "Interest exploration and investments in education: Experimental evidence from Cambodia," OSF Preprints k6tqr, Center for Open Science.
- Zhaonan Qu & Ruoxuan Xiong & Jizhou Liu & Guido Imbens, 2021. "Efficient Treatment Effect Estimation in Observational Studies under Heterogeneous Partial Interference," Papers 2107.12420, arXiv.org, revised Jun 2022.
- 'Agoston Reguly, 2021. "Heterogeneous Treatment Effects in Regression Discontinuity Designs," Papers 2106.11640, arXiv.org, revised Oct 2021.
- Nathan Kallus & Angela Zhou, 2021. "Minimax-Optimal Policy Learning Under Unobserved Confounding," Management Science, INFORMS, vol. 67(5), pages 2870-2890, May.
- Aldo Gael Carranza & Susan Athey, 2023. "Federated Offline Policy Learning with Heterogeneous Observational Data," Papers 2305.12407, arXiv.org.
- Sung Jae Jun & Sokbae Lee, 2020. "Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions," Papers 2004.08318, arXiv.org, revised Oct 2023.
- Oliver Hines & Stijn Vansteelandt & Karla Diaz-Ordaz, 2021. "Robust Inference for Mediated Effects in Partially Linear Models," Psychometrika, Springer;The Psychometric Society, vol. 86(2), pages 595-618, June.
- Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey & James M. Robins, 2022.
"Locally Robust Semiparametric Estimation,"
Econometrica, Econometric Society, vol. 90(4), pages 1501-1535, July.
- Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey, 2016. "Locally robust semiparametric estimation," CeMMAP working papers CWP31/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey & James M. Robins, 2016. "Locally Robust Semiparametric Estimation," Papers 1608.00033, arXiv.org, revised Aug 2020.
- Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey & James M. Robins, 2018. "Locally robust semiparametric estimation," CeMMAP working papers CWP30/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey, 2016. "Locally robust semiparametric estimation," CeMMAP working papers 31/16, Institute for Fiscal Studies.
- Jong Hee Park & Byung Koo Kim, 2020. "Why your neighbor matters: Positions in preferential trade agreement networks and export growth in global value chains," Economics and Politics, Wiley Blackwell, vol. 32(3), pages 381-410, November.
- Ziwei Mei & Zhentao Shi, 2022. "On LASSO for High Dimensional Predictive Regression," Papers 2212.07052, arXiv.org, revised Jan 2024.
- Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2022.
"Urban economics in a historical perspective: Recovering data with machine learning,"
Regional Science and Urban Economics, Elsevier, vol. 94(C).
- Gobillon, Laurent & Combes, Pierre-Philippe & Zylberberg, Yanos, 2020. "Urban economics in a historical perspective: Recovering data with machine learning," CEPR Discussion Papers 15308, C.E.P.R. Discussion Papers.
- Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2021. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," IZA Discussion Papers 14392, Institute of Labor Economics (IZA).
- Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2022. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," PSE-Ecole d'économie de Paris (Postprint) halshs-03673240, HAL.
- Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2022. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," Post-Print halshs-03673240, HAL.
- Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2021. "Urban economics in a historical perspective: Recovering data with machine learning," Working Papers halshs-03231786, HAL.
- Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2021. "Urban economics in a historical perspective: Recovering data with machine learning," PSE Working Papers halshs-03231786, HAL.
- Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2022. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," SciencePo Working papers Main halshs-03673240, HAL.
- Vira Semenova, 2017. "Debiased Machine Learning of Set-Identified Linear Models," Papers 1712.10024, arXiv.org, revised Dec 2022.
- Soren Blomquist & Anil Kumar & Che-Yuan Liang & Whitney K. Newey, 2022.
"Nonlinear Budget Set Regressions for the Random Utility Model,"
Working Papers
2219, Federal Reserve Bank of Dallas.
- Sören Blomquist & Anil Kumar & Che-Yuan Liang & Whitney Newey, 2023. "Nonlinear Budget Set Regressions for the Random Utility Model," NBER Working Papers 31194, National Bureau of Economic Research, Inc.
- Bilgin, Rumeysa, 2023. "The Selection Of Control Variables In Capital Structure Research With Machine Learning," SocArXiv e26qf, Center for Open Science.
- Chaohua Dong & Jiti Gao & Oliver Linton, 2017.
"High dimensional semiparametric moment restriction models,"
Monash Econometrics and Business Statistics Working Papers
17/17, Monash University, Department of Econometrics and Business Statistics.
- Chaohua Dong & Jiti Gao & Oliver Linton, 2018. "High dimensional semiparametric moment restriction models," CeMMAP working papers CWP04/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Dong, C. & Gao, J. & Linton, O., 2018. "High Dimensional Semiparametric Moment Restriction Models," Cambridge Working Papers in Economics 1881, Faculty of Economics, University of Cambridge.
- Chaohua Dong & Jiti Gao & Oliver Linton, 2018. "High dimensional semiparametric moment restriction models," CeMMAP working papers CWP69/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Chaohua Dong & Jiti Gao & Oliver Linton, 2018. "High dimensional semiparametric moment restriction models," Monash Econometrics and Business Statistics Working Papers 23/18, Monash University, Department of Econometrics and Business Statistics.
- Paul Clarke & Annalivia Polselli, 2023. "Double Machine Learning for Static Panel Models with Fixed Effects," Papers 2312.08174, arXiv.org, revised Dec 2023.
- Ziwei Cong & Jia Liu & Puneet Manchanda, 2021. "The Role of "Live" in Livestreaming Markets: Evidence Using Orthogonal Random Forest," Papers 2107.01629, arXiv.org, revised Sep 2022.
- Alejandro Sanchez-Becerra, 2023. "Robust inference for the treatment effect variance in experiments using machine learning," Papers 2306.03363, arXiv.org.
- Davide Viviano, 2019. "Policy Targeting under Network Interference," Papers 1906.10258, arXiv.org, revised Apr 2024.
- Amit Sharma & Emre Kiciman, 2020. "DoWhy: An End-to-End Library for Causal Inference," Papers 2011.04216, arXiv.org.
- Victor Chernozhukov & Chen Huang & Weining Wang, 2021. "Uniform Inference on High-dimensional Spatial Panel Networks," Papers 2105.07424, arXiv.org, revised Sep 2023.
- Juodis, Artūras & Sarafidis, Vasilis, 2022.
"An incidental parameters free inference approach for panels with common shocks,"
Journal of Econometrics, Elsevier, vol. 229(1), pages 19-54.
- Juodis, Arturas & Sarafidis, Vasilis, 2020. "An Incidental Parameters Free Inference Approach for Panels with Common Shocks," MPRA Paper 104906, University Library of Munich, Germany.
- Yulia Kotlyarova & Marcia M. A. Schafgans & Victoria Zinde-Walsh, 2021.
"Rates of Expansions for Functional Estimators,"
Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 121-139, December.
- Kotlyarova, Yulia & Schafgans, Marcia M.A. & Zinde-Walsh, Victoria, 2021. "Rates of expansions for functional estimators," LSE Research Online Documents on Economics 113436, London School of Economics and Political Science, LSE Library.
- Feng, Sanying & Kong, Kaidi & Kong, Yinfei & Li, Gaorong & Wang, Zhaoliang, 2022. "Statistical inference of heterogeneous treatment effect based on single-index model," Computational Statistics & Data Analysis, Elsevier, vol. 175(C).
- 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.
- Susan Athey & Guido Imbens & Jonas Metzger & Evan Munro, 2019. "Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations," Papers 1909.02210, arXiv.org, revised Jul 2020.
- Susan Athey & Stefan Wager, 2021.
"Policy Learning With Observational Data,"
Econometrica, Econometric Society, vol. 89(1), pages 133-161, January.
- Susan Athey & Stefan Wager, 2017. "Policy Learning with Observational Data," Papers 1702.02896, arXiv.org, revised Sep 2020.
- Waddell, Glen R. & McDonough, Robert, 2022. "Mean Convergence, Combinatorics, and Grade-Point Averages," IZA Discussion Papers 15414, Institute of Labor Economics (IZA).
- Xinkun Nie & Stefan Wager, 2017. "Quasi-Oracle Estimation of Heterogeneous Treatment Effects," Papers 1712.04912, arXiv.org, revised Aug 2020.
- 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.
- Oyenubi, Adeola & Kollamparambil, Umakrishnan, 2023. "Does noncompliance with COVID-19 regulations impact the depressive symptoms of others?," Economic Modelling, Elsevier, vol. 120(C).
- Kea BARET, 2021. "Fiscal rules’ compliance and Social Welfare," Working Papers of BETA 2021-38, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
- Munday, Tim & Brookes, James, 2021. "Mark my words: the transmission of central bank communication to the general public via the print media," Bank of England working papers 944, Bank of England.
- Masahiro Kato & Shota Yasui & Kenichiro McAlinn, 2020. "The Adaptive Doubly Robust Estimator for Policy Evaluation in Adaptive Experiments and a Paradox Concerning Logging Policy," Papers 2010.03792, arXiv.org, revised Jun 2021.
- Yijun Li & Cheuk Hang Leung & Xiangqian Sun & Chaoqun Wang & Yiyan Huang & Xing Yan & Qi Wu & Dongdong Wang & Zhixiang Huang, 2023. "The Causal Impact of Credit Lines on Spending Distributions," Papers 2312.10388, arXiv.org.
- Juan Carlos Escanciano & Telmo P'erez-Izquierdo, 2023. "Automatic Locally Robust Estimation with Generated Regressors," Papers 2301.10643, arXiv.org, revised Nov 2023.
- Mert Demirer & Vasilis Syrgkanis & Greg Lewis & Victor Chernozhukov, 2019.
"Semi-Parametric Efficient Policy Learning with Continuous Actions,"
CeMMAP working papers
CWP34/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Mert Demirer & Vasilis Syrgkanis & Greg Lewis & Victor Chernozhukov, 2019. "Semi-Parametric Efficient Policy Learning with Continuous Actions," Papers 1905.10116, arXiv.org, revised Jul 2019.
- Black, Dan A. & Grogger, Jeffrey & Kirchmaier, Tom & Sanders, Koen, 2023.
"Criminal charges, risk assessment and violent recidivism in cases of domestic abuse,"
LSE Research Online Documents on Economics
121374, London School of Economics and Political Science, LSE Library.
- Dan A. Black & Jeffrey Grogger & Tom Kirchmaier & Koen Sanders, 2023. "Criminal Charges, Risk Assessment, and Violent Recidivism in Cases of Domestic Abuse," NBER Working Papers 30884, National Bureau of Economic Research, Inc.
- Dan A. Black & Jeffrey Grogger & Tom Kirchmaier & Koen Sanders, 2023. "Criminal charges, risk assessment and violent recidivism in cases of domestic abuse," CEP Discussion Papers dp1897, Centre for Economic Performance, LSE.
- 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, Institute of Labor Economics (IZA).
- Hidehiko Ichimura & Whitney K. Newey, 2022.
"The influence function of semiparametric estimators,"
Quantitative Economics, Econometric Society, vol. 13(1), pages 29-61, January.
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