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Program Evaluation and Causal Inference with High-Dimensional Data
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Cited by:
- Su, Miaomiao & Wang, Ruoyu & Wang, Qihua, 2022. "A two-stage optimal subsampling estimation for missing data problems with large-scale data," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
- Francesca Micocci & Armando Rungi, 2021.
"Predicting Exporters with Machine Learning,"
Papers
2107.02512, arXiv.org, revised Sep 2022.
- Francesca Micocci & Armando Rungi, 2021. "Predicting Exporters with Machine Learning," Working Papers 03/2021, IMT School for Advanced Studies Lucca, revised Jul 2021.
- Kaspar Wüthrich, 2020.
"A Comparison of Two Quantile Models With Endogeneity,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 443-456, April.
- Kaspar W thrich, 2014. "A Comparison of two Quantile Models with Endogeneity," Diskussionsschriften dp1408, Universitaet Bern, Departement Volkswirtschaft.
- Wüthrich, Kaspar, 2020. "A Comparison of Two Quantile Models With Endogeneity," University of California at San Diego, Economics Working Paper Series qt0q43931f, Department of Economics, UC San Diego.
- Shakeeb Khan & Xiaoying Lan & Elie Tamer & Qingsong Yao, 2021. "Estimating High Dimensional Monotone Index Models by Iterative Convex Optimization1," Papers 2110.04388, arXiv.org, revised Feb 2023.
- Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP54/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- 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.
- Denis Fougère & Nicolas Jacquemet, 2019. "Causal Inference and Impact Evaluation," SciencePo Working papers Main hal-02866828, HAL.
- Denis Fougère & Nicolas Jacquemet, 2019. "Causal Inference and Impact Evaluation," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-02866828, HAL.
- Denis Fougère & Nicolas Jacquemet, 2019. "Causal Inference and Impact Evaluation," Post-Print hal-02866828, HAL.
- Denis Fougère & Nicolas Jacquemet, 2019. "Causal Inference and Impact Evaluation," PSE-Ecole d'économie de Paris (Postprint) hal-02866828, HAL.
- Rahul Singh, 2021. "Kernel Ridge Riesz Representers: Generalization, Mis-specification, and the Counterfactual Effective Dimension," Papers 2102.11076, arXiv.org, revised Jul 2024.
- Lechner, Michael, 2018.
"Modified Causal Forests for Estimating Heterogeneous Causal Effects,"
IZA Discussion Papers
12040, Institute of Labor Economics (IZA).
- Lechner, Michael, 2019. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," CEPR Discussion Papers 13430, C.E.P.R. Discussion Papers.
- Lechner, Michael, 2019. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," Economics Working Paper Series 1901, University of St. Gallen, School of Economics and Political Science.
- Michael Lechner, 2018. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," Papers 1812.09487, arXiv.org, revised Jul 2019.
- 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.
- Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Sep 2023.
- Byron Botha & Rulof Burger & Kevin Kotzé & Neil Rankin & Daan Steenkamp, 2023.
"Big data forecasting of South African inflation,"
Empirical Economics, Springer, vol. 65(1), pages 149-188, July.
- Byron Botha & Kevin Kotze & Neil Rankin & Rulof P. Burger, 2022. "Big data forecasting of South African inflation," Working Papers 873, Economic Research Southern Africa.
- Byron Botha & Rulof Burger & Kevin Kotze & Neil Rankin & Daan Steenkamp, 2022. "Big data forecasting of South African inflation," School of Economics Macroeconomic Discussion Paper Series 2022-03, School of Economics, University of Cape Town.
- Byron Botha & Rulof Burger & Kevin Kotz & Neil Rankin & Daan Steenkamp, 2022. "Big data forecasting of South African inflation," Working Papers 11022, South African Reserve Bank.
- Berden, Carolien & Croes, R. & Kemp, R. & Mikkers, Misja & van der Noll, Rob & Shestalova, V. & Svitak, Jan, 2019.
"Hospital Competition in the Netherlands : An Empirical Investigation,"
Discussion Paper
2019-008, Tilburg University, Tilburg Law and Economic Center.
- Berden, Carolien & Croes, R. & Kemp, R. & Mikkers, Misja & van der Noll, Rob & Shestalova, V. & Svitak, Jan, 2019. "Hospital Competition in the Netherlands : An Empirical Investigation," Other publications TiSEM e30db5a4-5c1c-450b-8f1d-6, Tilburg University, School of Economics and Management.
- Berden, Carolien & Croes, R. & Kemp, R. & Mikkers, Misja & van der Noll, Rob & Shestalova, V. & Svitak, Jan, 2019. "Hospital Competition in the Netherlands : An Empirical Investigation," Discussion Paper 2019-018, Tilburg University, Center for Economic Research.
- Berden, Carolien & Croes, R. & Kemp, R. & Mikkers, Misja & van der Noll, Rob & Shestalova, V. & Svitak, Jan, 2019. "Hospital Competition in the Netherlands : An Empirical Investigation," Other publications TiSEM 5302ab6a-9099-4b5d-9874-8, Tilburg University, School of Economics and Management.
- Breunig, Christoph & Mammen, Enno & Simoni, Anna, 2020.
"Ill-posed estimation in high-dimensional models with instrumental variables,"
Journal of Econometrics, Elsevier, vol. 219(1), pages 171-200.
- Christoph Breunig & Enno Mammen & Anna Simoni, 2018. "Ill-posed Estimation in High-Dimensional Models with Instrumental Variables," Papers 1806.00666, arXiv.org, revised Aug 2020.
- Christoph Breunig & Enno Mammen & Anna Simoni, 2020. "Ill-posed estimation in high-dimensional models with instrumental variables," Post-Print hal-03089879, HAL.
- NARITA Yusuke & YATA Kohei, 2021.
"Algorithm is Experiment: Machine Learning, Market Design, and Policy Eligibility Rules,"
Discussion papers
21057, Research Institute of Economy, Trade and Industry (RIETI).
- Yusuke Narita & Kohei Yata, 2021. "Algorithm is Experiment: Machine Learning, Market Design, and Policy Eligibility Rules," Cowles Foundation Discussion Papers 2283, Cowles Foundation for Research in Economics, Yale University.
- Yusuke Narita & Kohei Yata, 2021. "Algorithm is Experiment: Machine Learning, Market Design, and Policy Eligibility Rules," Working Papers 2021-022, Human Capital and Economic Opportunity Working Group.
- Yusuke Narita & Kohei Yata, 2021. "Algorithm as Experiment: Machine Learning, Market Design, and Policy Eligibility Rules," Papers 2104.12909, arXiv.org, revised Dec 2023.
- Yamin Ahmad & Adam Check & Ming Chien Lo, 2024. "Unit Roots in Macroeconomic Time Series: A Comparison of Classical, Bayesian and Machine Learning Approaches," Computational Economics, Springer;Society for Computational Economics, vol. 63(6), pages 2139-2173, June.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022.
"How is machine learning useful for macroeconomic forecasting?,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2019. "How is Machine Learning Useful for Macroeconomic Forecasting?," CIRANO Working Papers 2019s-22, CIRANO.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & St'ephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Papers 2008.12477, arXiv.org.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Working Papers 20-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Aug 2020.
- Undral Byambadalai & Tatsushi Oka & Shota Yasui, 2024. "Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction," Papers 2407.16037, arXiv.org.
- Wüthrich, Kaspar, 2019.
"A closed-form estimator for quantile treatment effects with endogeneity,"
Journal of Econometrics, Elsevier, vol. 210(2), pages 219-235.
- Wüthrich, Kaspar, 2019. "A closed-form estimator for quantile treatment effects with endogeneity," University of California at San Diego, Economics Working Paper Series qt99n9197q, Department of Economics, UC San Diego.
- 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).
- 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.
- Lihua Lei & Brad Ross, 2023. "Estimating Counterfactual Matrix Means with Short Panel Data," Papers 2312.07520, arXiv.org, revised May 2024.
- Yumou Qiu & Jing Tao & Xiao‐Hua Zhou, 2021. "Inference of heterogeneous treatment effects using observational data with high‐dimensional covariates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(5), pages 1016-1043, November.
- Yue, Lili & Li, Gaorong & Lian, Heng & Wan, Xiang, 2019. "Regression adjustment for treatment effect with multicollinearity in high dimensions," Computational Statistics & Data Analysis, Elsevier, vol. 134(C), pages 17-35.
- Semenova, Vira, 2023. "Debiased machine learning of set-identified linear models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1725-1746.
- Zheng Fang & Juwon Seo, 2019. "A Projection Framework for Testing Shape Restrictions That Form Convex Cones," Papers 1910.07689, arXiv.org, revised Sep 2021.
- Mazzocchi, Mario & Capacci, Sara & Biondi, Beatrice, 2022. "Causal inference on the impact of nutrition policies using observational data," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), vol. 11(1), April.
- 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.
- Neng-Chieh Chang, 2018. "Semiparametric Difference-in-Differences with Potentially Many Control Variables," Papers 1812.10846, arXiv.org, revised Jan 2019.
- Matias D. Cattaneo & Michael Jansson & Whitney K. Newey, 2018.
"Inference in Linear Regression Models with Many Covariates and Heteroscedasticity,"
Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1350-1361, July.
- Matias D. Cattaneo & Michael Jansson & Whitney K. Newey, 2015. "Inference in Linear Regression Models with Many Covariates and Heteroskedasticity," Papers 1507.02493, arXiv.org, revised Jan 2017.
- Matias Cattaneo & Michael Jansson & Whitney K. Newey, 2017. "Inference in linear regression models with many covariates and heteroskedasticity," CeMMAP working papers 03/17, Institute for Fiscal Studies.
- Cattaneo, Matias D & Jansson, Michael & Newey, Whitney K, 2018. "Inference in Linear Regression Models with Many Covariates and Heteroscedasticity," Department of Economics, Working Paper Series qt6rp7p9gs, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
- Matias Cattaneo & Michael Jansson & Whitney K. Newey, 2017. "Inference in linear regression models with many covariates and heteroskedasticity," CeMMAP working papers CWP03/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Matias D Cattaneo & Michael Jansson & Xinwei Ma, 2019.
"Two-Step Estimation and Inference with Possibly Many Included Covariates,"
The Review of Economic Studies, Review of Economic Studies Ltd, vol. 86(3), pages 1095-1122.
- Matias D. Cattaneo & Michael Jansson & Xinwei Ma, 2018. "Two-Step Estimation and Inference with Possibly Many Included Covariates," Papers 1807.10100, arXiv.org.
- Cattaneo, Matias D & Jansson, Michael & Ma, Xinwei, 2019. "Two-Step Estimation and Inference with Possibly Many Included Covariates," University of California at San Diego, Economics Working Paper Series qt86c7x315, Department of Economics, UC San Diego.
- Cattaneo, Matias D & Jansson, Michael & Ma, Xinwei, 2019. "Two-Step Estimation and Inference with Possibly Many Included Covariates," Department of Economics, Working Paper Series qt86c7x315, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
- Shi, Chengchun & Wan, Runzhe & Song, Ge & Luo, Shikai & Zhu, Hongtu & Song, Rui, 2023. "A multiagent reinforcement learning framework for off-policy evaluation in two-sided markets," LSE Research Online Documents on Economics 117174, London School of Economics and Political Science, LSE Library.
- Belloni, Alexandre. & Chen, Mingli & Chernozhukov, Victor, 2016.
"Quantile Graphical Models: Prediction and Conditional Independence with Applications to Financial Risk Management,"
The Warwick Economics Research Paper Series (TWERPS)
1125, University of Warwick, Department of Economics.
- Belloni, Alexandre & Chen, Mingli & Chernozhukov, Victor, 2016. "Quantile Graphical Models : Prediction and Conditional Independence with Applications to Financial Risk Management," Economic Research Papers 269321, University of Warwick - Department of Economics.
- Michael Pollmann, 2020. "Causal Inference for Spatial Treatments," Papers 2011.00373, arXiv.org, revised Jan 2023.
- Carvalho, Carlos & Masini, Ricardo & Medeiros, Marcelo C., 2018.
"ArCo: An artificial counterfactual approach for high-dimensional panel time-series data,"
Journal of Econometrics, Elsevier, vol. 207(2), pages 352-380.
- Carlos Viana de Carvalho & Ricardo Masini & Marcelo Cunha Medeiros, 2016. "ARCO: an artificial counterfactual approach for high-dimensional panel time-series data," Textos para discussão 653, Department of Economics PUC-Rio (Brazil).
- Carvalho, Carlos Viana de & Masini, Ricardo Pereira & Medeiros, Marcelo C., 2017. "Arco: an artificial counterfactual approach for high-dimensional panel time-series data," Textos para discussão 454, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
- Neng-Chieh Chang, 2020. "The Mode Treatment Effect," Papers 2007.11606, arXiv.org.
- Marianne Bl'ehaut & Xavier D'Haultfoeuille & J'er'emy L'Hour & Alexandre B. Tsybakov, 2020.
"An alternative to synthetic control for models with many covariates under sparsity,"
Papers
2005.12225, arXiv.org, revised Jun 2021.
- Marianne BLÉHAUT & Xavier D'HAULTFOEUILLE & Jérémy L'HOUR & Alexandre B. TSYBAKOV, 2020. "An alternative to synthetic control for models with many covariates under sparsity," Working Papers 2020-17, Center for Research in Economics and Statistics.
- 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).
- 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.
- 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.
- 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.
- 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.
- Qizhao Chen & Vasilis Syrgkanis & Morgane Austern, 2022. "Debiased Machine Learning without Sample-Splitting for Stable Estimators," Papers 2206.01825, arXiv.org, revised Nov 2022.
- Zhengyuan Zhou & Susan Athey & Stefan Wager, 2023.
"Offline Multi-Action Policy Learning: Generalization and Optimization,"
Operations Research, INFORMS, vol. 71(1), pages 148-183, January.
- Zhou, Zhengyuan & Athey, Susan & Wager, Stefan, 2018. "Offline Multi-Action Policy Learning: Generalization and Optimization," Research Papers 3734, Stanford University, Graduate School of Business.
- Zhengyuan Zhou & Susan Athey & Stefan Wager, 2018. "Offline Multi-Action Policy Learning: Generalization and Optimization," Papers 1810.04778, arXiv.org, revised Nov 2018.
- Ying-Ying Lee & Chu-An Liu, 2024. "Lee Bounds with a Continuous Treatment in Sample Selection," Papers 2411.04312, arXiv.org, revised Jan 2025.
- 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.
- Hartley, Robert Paul & Lamarche, Carlos, 2018.
"Behavioral responses and welfare reform: Evidence from a randomized experiment,"
Labour Economics, Elsevier, vol. 54(C), pages 135-151.
- Hartley, Robert Paul & Lamarche, Carlos, 2017. "Behavioral Responses and Welfare Reform: Evidence from a Randomized Experiment," IZA Discussion Papers 10905, Institute of Labor Economics (IZA).
- Sant’Anna, Pedro H.C. & Song, Xiaojun, 2019.
"Specification tests for the propensity score,"
Journal of Econometrics, Elsevier, vol. 210(2), pages 379-404.
- Pedro H. C. Sant'Anna & Xiaojun Song, 2016. "Specification Tests for the Propensity Score," Papers 1611.06217, arXiv.org, revised Feb 2019.
- Tomasz Olma, 2021. "Nonparametric Estimation of Truncated Conditional Expectation Functions," Papers 2109.06150, arXiv.org.
- Entorf, Horst & Hou, Jia, 2018.
"Financial education for the disadvantaged? A review,"
SAFE Working Paper Series
205, Leibniz Institute for Financial Research SAFE.
- Entorf, Horst & Hou, Jia, 2018. "Financial Education for the Disadvantaged? A Review," IZA Discussion Papers 11515, Institute of Labor Economics (IZA).
- Brantly Callaway & Pedro H. C. Sant'Anna, 2018. "Difference-in-Differences with Multiple Time Periods and an Application on the Minimum Wage and Employment," DETU Working Papers 1804, Department of Economics, Temple University.
- 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.
- Ahrens, Achim & Hansen, Christian B. & Schaffer, Mark E & Wiemann, Thomas, 2024.
"Model Averaging and Double Machine Learning,"
IZA Discussion Papers
16714, Institute of Labor Economics (IZA).
- Achim Ahrens & Christian B. Hansen & Mark E. Schaffer & Thomas Wiemann, 2024. "Model Averaging and Double Machine Learning," Papers 2401.01645, arXiv.org, revised Sep 2024.
- Galbraith, John W. & Zinde-Walsh, Victoria, 2020. "Simple and reliable estimators of coefficients of interest in a model with high-dimensional confounding effects," Journal of Econometrics, Elsevier, vol. 218(2), pages 609-632.
- Sloczynski, Tymon & Uysal, Derya & Wooldridge, Jeffrey M., 2022.
"Abadie's Kappa and Weighting Estimators of the Local Average Treatment Effect,"
IZA Discussion Papers
15241, Institute of Labor Economics (IZA).
- Derya Uysal, 2023. "Abadie's kappa and weighting estimators of the local average treatment effect," Economics Virtual Symposium 2023 01, Stata Users Group.
- Tymon Sloczynski & Derya Uysal & Jeffrey Wooldridge, 2023. "Abadie's Kappa and Weighting Estimators of the Local Average Treatment Effect," Rationality and Competition Discussion Paper Series 424, CRC TRR 190 Rationality and Competition.
- Guo, Xu & Li, Runze & Liu, Jingyuan & Zeng, Mudong, 2023. "Statistical inference for linear mediation models with high-dimensional mediators and application to studying stock reaction to COVID-19 pandemic," Journal of Econometrics, Elsevier, vol. 235(1), pages 166-179.
- Callaway, Brantly & Sant’Anna, Pedro H.C., 2021.
"Difference-in-Differences with multiple time periods,"
Journal of Econometrics, Elsevier, vol. 225(2), pages 200-230.
- Brantly Callaway & Pedro H. C. Sant'Anna, 2018. "Difference-in-Differences with Multiple Time Periods," Papers 1803.09015, arXiv.org, revised Dec 2020.
- Hiroaki Kaido & Kaspar Wüthrich, 2021.
"Decentralization estimators for instrumental variable quantile regression models,"
Quantitative Economics, Econometric Society, vol. 12(2), pages 443-475, May.
- Hiroaki Kaido & Kaspar Wüthrich, 2018. "Decentralization estimators for instrumental variable quantile regression models," CeMMAP working papers CWP72/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Kaido, Hiroaki & Wüthrich, Kaspar, 2021. "Decentralization estimators for instrumental variable quantile regression models," University of California at San Diego, Economics Working Paper Series qt362921wv, Department of Economics, UC San Diego.
- Hiroaki Kaido & Kaspar Wuthrich, 2018. "Decentralization Estimators for Instrumental Variable Quantile Regression Models," Papers 1812.10925, arXiv.org, revised Sep 2020.
- Hiroaki Kaido & Kaspar Wüthrich, 2019. "Decentralization estimators for instrumental variable quantile regression models," CeMMAP working papers CWP42/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Denis Fougère & Nicolas Jacquemet, 2020.
"Policy Evaluation Using Causal Inference Methods,"
Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers)
hal-03455978, HAL.
- Denis Fougère & Nicolas Jacquemet, 2021. "Policy Evaluation Using Causal Inference Methods," PSE-Ecole d'économie de Paris (Postprint) hal-03098058, HAL.
- Denis Fougère & Nicolas Jacquemet, 2021. "Policy Evaluation Using Causal Inference Methods," SciencePo Working papers Main hal-03098058, HAL.
- Fougère, Denis & Jacquemet, Nicolas, 2020. "Policy Evaluation Using Causal Inference Methods," IZA Discussion Papers 12922, Institute of Labor Economics (IZA).
- Denis Fougère & Nicolas Jacquemet, 2020. "Policy Evaluation Using Causal Inference Methods," SciencePo Working papers Main hal-03455978, HAL.
- Denis Fougère & Nicolas Jacquemet, 2021. "Policy Evaluation Using Causal Inference Methods," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03098058, HAL.
- Denis Fougère & Nicolas Jacquemet, 2021. "Policy Evaluation Using Causal Inference Methods," Post-Print hal-03098058, HAL.
- Denis Fougère & Nicolas Jacquemet, 2020. "Policy Evaluation Using Causal Inference Methods," Working Papers hal-03455978, HAL.
- Luong, Hoa & Khedmati, Mehdi & Nguyen, Lan Anh & Nigmonov, Asror & Ovi, Nafisa Zabeen & Shams, Syed, 2023. "CEO-director ties and board gender diversity: US evidence," Journal of Behavioral and Experimental Finance, Elsevier, vol. 40(C).
- Rahul Singh & Liyang Sun, 2024.
"Double robustness for complier parameters and a semi-parametric test for complier characteristics,"
The Econometrics Journal, Royal Economic Society, vol. 27(1), pages 1-20.
- Rahul Singh & Liyang Sun, 2019. "Double Robustness for Complier Parameters and a Semiparametric Test for Complier Characteristics," Papers 1909.05244, arXiv.org, revised Dec 2022.
- Herrera, Diego & Cunniff, Shannon & DuPont, Carolyn & Cohen, Benjamin & Gangi, Dakota & Kar, Devyani & Peyronnin Snider, Natalie & Rojas, Victor & Wyerman, Jim & Norriss, Jessie & Mountenot, Marshall, 2019. "Designing an environmental impact bond for wetland restoration in Louisiana," Ecosystem Services, Elsevier, vol. 35(C), pages 260-276.
- 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.
- Hünermund Paul & Louw Beyers & Caspi Itamar, 2023.
"Double machine learning and automated confounder selection: A cautionary tale,"
Journal of Causal Inference, De Gruyter, vol. 11(1), pages 1-12, January.
- 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.
- Cerqua, Augusto & Letta, Marco, 2022.
"Local inequalities of the COVID-19 crisis,"
Regional Science and Urban Economics, Elsevier, vol. 92(C).
- Cerqua, Augusto & Letta, Marco, 2021. "Local inequalities of the COVID-19 crisis," GLO Discussion Paper Series 875, Global Labor Organization (GLO).
- Valente, Marica, 2023.
"Policy evaluation of waste pricing programs using heterogeneous causal effect estimation,"
Journal of Environmental Economics and Management, Elsevier, vol. 117(C).
- Marica Valente, 2020. "Policy evaluation of waste pricing programs using heterogeneous causal effect estimation," Papers 2010.01105, arXiv.org, revised Nov 2022.
- Marica Valente, 2021. "Policy Evaluation of Waste Pricing Programs Using Heterogeneous Causal Effect Estimation," Discussion Papers of DIW Berlin 1980, DIW Berlin, German Institute for Economic Research.
- Barbara Felderer & Jannis Kueck & Martin Spindler, 2021. "Big Data meets Causal Survey Research: Understanding Nonresponse in the Recruitment of a Mixed-mode Online Panel," Papers 2102.08994, arXiv.org.
- Dylan Brewer & Alyssa Carlson, 2024.
"Addressing sample selection bias for machine learning methods,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 383-400, April.
- Dylan Brewer & Alyssa Carlson, 2021. "Addressing Sample Selection Bias for Machine Learning Methods," Working Papers 2102, Department of Economics, University of Missouri.
- Dylan Brewer & Alyssa Carlson, 2023. "Addressing Sample Selection Bias for Machine Learning Methods," Working Papers 2302, Department of Economics, University of Missouri.
- Dylan Brewer & Alyssa Carlson, 2023. "Addressing Sample Selection Bias for Machine Learning Methods," Working Papers 2310, Department of Economics, University of Missouri.
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