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Doubly robust uniform confidence band for the conditional average treatment effect function

Citations

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Cited by:

  1. Riccardo Di Francesco, 2022. "Aggregation Trees," CEIS Research Paper 546, Tor Vergata University, CEIS, revised 20 Nov 2023.
  2. Zongwu Cai & Ying Fang & Ming Lin & Shengfang Tang, 2021. "Estimating Partially Conditional Quantile Treatment Effects," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202103, University of Kansas, Department of Economics, revised Jan 2021.
  3. Lechner, Michael, 2018. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," IZA Discussion Papers 12040, Institute of Labor Economics (IZA).
  4. Patrice Bougette & Oliver Budzinski & Frédéric Marty, 2023. "In the Light of Dynamic Competition: Should We Make Merger Remedies More Flexible?," Working Papers AFED 23-01, Association Francaise d'Economie du Droit (AFED).
  5. Gabriel Okasa, 2022. "Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance," Papers 2201.12692, arXiv.org.
  6. Pedro H. C. Sant’Anna, 2021. "Nonparametric Tests for Treatment Effect Heterogeneity With Duration Outcomes," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(3), pages 816-832, July.
  7. 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.
  8. Sasaki, Yuya & Ura, Takuya, 2023. "Estimation and inference for policy relevant treatment effects," Journal of Econometrics, Elsevier, vol. 234(2), pages 394-450.
  9. Anthony Strittmatter, 2018. "What Is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation?," Papers 1812.06533, arXiv.org, revised Dec 2021.
  10. Michael Lechner & Jana Mareckova, 2022. "Modified Causal Forest," Papers 2209.03744, arXiv.org.
  11. 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.
  12. Sant’Anna, Pedro H.C. & Zhao, Jun, 2020. "Doubly robust difference-in-differences estimators," Journal of Econometrics, Elsevier, vol. 219(1), pages 101-122.
  13. Masahiro Kato, 2024. "Triple/Debiased Lasso for Statistical Inference of Conditional Average Treatment Effects," Papers 2403.03240, arXiv.org.
  14. Benjamin Monnery & Alexandre Chirat, 2024. "Trust in the Fight Against Political Corruption: A Survey Experiment among Citizens and Experts," Working Papers AFED 24-02, Association Francaise d'Economie du Droit (AFED).
  15. Zongwu Cai & Ying Fang & Ming Lin & Shengfang Tang, 2021. "A Nonparametric Test for Testing Heterogeneity in Conditional Quantile Treatment Effects," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202117, University of Kansas, Department of Economics, revised Aug 2021.
  16. 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.
  17. Qingliang Fan & Yu-Chin Hsu & Robert P. Lieli & Yichong Zhang, 2022. "Estimation of Conditional Average Treatment Effects With High-Dimensional Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 313-327, January.
  18. Shengfang Tang & Zongwu Cai & Ying Fang & Ming Lin, 2020. "A New Quantile Treatment Effect Model for Studying Smoking Effect on Birth Weight During Mother's Pregnancy," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202003, University of Kansas, Department of Economics, revised Feb 2020.
  19. 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.
  20. 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).
  21. Yang Ning & Sida Peng & Jing Tao, 2020. "Doubly Robust Semiparametric Difference-in-Differences Estimators with High-Dimensional Data," Papers 2009.03151, arXiv.org.
  22. Michael Zimmert & Michael Lechner, 2019. "Nonparametric estimation of causal heterogeneity under high-dimensional confounding," Papers 1908.08779, arXiv.org.
  23. Zhou, Niwen & Guo, Xu & Zhu, Lixing, 2024. "Significance test for semiparametric conditional average treatment effects and other structural functions," Computational Statistics & Data Analysis, Elsevier, vol. 189(C).
  24. Daniel Jacob, 2019. "Group Average Treatment Effects for Observational Studies," Papers 1911.02688, arXiv.org, revised Mar 2020.
  25. Shengfang Tang & Zongwu Cai & Ying Fang & Ming Lin, 2019. "Testing Unconfoundedness Assumption Using Auxiliary Variables," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201905, University of Kansas, Department of Economics, revised Mar 2019.
  26. Michael Lechner & Jana Mareckova, 2024. "Comprehensive Causal Machine Learning," Papers 2405.10198, arXiv.org.
  27. 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.
  28. Riccardo Di Francesco, 2024. "Aggregation Trees," Papers 2410.11408, arXiv.org.
  29. 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.
  30. Pedro H. C. Sant'Anna & Xiaojun Song, 2020. "Specification tests for generalized propensity scores using double projections," Papers 2003.13803, arXiv.org, revised Apr 2023.
  31. Benjamin Monnery & Alexandre Chirat, 2023. "Trust in the fight against political corruption: A survey experiment among citizens and experts," EconomiX Working Papers 2023-11, University of Paris Nanterre, EconomiX.
  32. 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.
  33. Sung Jae Jun & Sokbae Lee, 2022. "Average Adjusted Association: Efficient Estimation with High Dimensional Confounders," Papers 2205.14048, arXiv.org, revised Apr 2023.
  34. Zongwu Cai & Ying Fang & Ming Lin & Shengfang Tang, 2020. "Inferences for Partially Conditional Quantile Treatment Effect Model," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202005, University of Kansas, Department of Economics, revised Feb 2020.
  35. Roth, Jonathan & Sant’Anna, Pedro H.C. & Bilinski, Alyssa & Poe, John, 2023. "What’s trending in difference-in-differences? A synthesis of the recent econometrics literature," Journal of Econometrics, Elsevier, vol. 235(2), pages 2218-2244.
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