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A Simple Method for Estimating Interactions Between a Treatment and a Large Number of Covariates

Citations

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Cited by:

  1. Shu Yang & Yilong Zhang & Guanghan Frank Liu & Qian Guan, 2023. "SMIM: A unified framework of survival sensitivity analysis using multiple imputation and martingale," Biometrics, The International Biometric Society, vol. 79(1), pages 230-240, March.
  2. Lechner, Michael, 2018. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," IZA Discussion Papers 12040, Institute of Labor Economics (IZA).
  3. 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.
  4. Shonosuke Sugasawa & Hisashi Noma, 2021. "Efficient screening of predictive biomarkers for individual treatment selection," Biometrics, The International Biometric Society, vol. 77(1), pages 249-257, March.
  5. Peng Wu & Donglin Zeng & Haoda Fu & Yuanjia Wang, 2020. "On using electronic health records to improve optimal treatment rules in randomized trials," Biometrics, The International Biometric Society, vol. 76(4), pages 1075-1086, December.
  6. 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.
  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, Institute of Labor Economics (IZA).
  8. 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.
  9. 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).
  10. Raaz Dwivedi & Yan Shuo Tan & Briton Park & Mian Wei & Kevin Horgan & David Madigan & Bin Yu, 2020. "Stable Discovery of Interpretable Subgroups via Calibration in Causal Studies," International Statistical Review, International Statistical Institute, vol. 88(S1), pages 135-178, December.
  11. Weibin Mo & Yufeng Liu, 2022. "Efficient learning of optimal individualized treatment rules for heteroscedastic or misspecified treatment‐free effect models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 440-472, April.
  12. Engel, Christoph, 2020. "Estimating heterogeneous reactions to experimental treatments," Journal of Economic Behavior & Organization, Elsevier, vol. 178(C), pages 124-147.
  13. Shi, Chengchun & Luo, Shikai & Le, Yuan & Zhu, Hongtu & Song, Rui, 2022. "Statistically efficient advantage learning for offline reinforcement learning in infinite horizons," LSE Research Online Documents on Economics 115598, London School of Economics and Political Science, LSE Library.
  14. Hyung G. Park & Danni Wu & Eva Petkova & Thaddeus Tarpey & R. Todd Ogden, 2023. "Bayesian Index Models for Heterogeneous Treatment Effects on a Binary Outcome," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(2), pages 397-418, July.
  15. Kirk Bansak, 2021. "Estimating causal moderation effects with randomized treatments and non‐randomized moderators," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 65-86, January.
  16. 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.
  17. Robin Gubela & Artem Bequé & Stefan Lessmann & Fabian Gebert, 2019. "Conversion Uplift in E-Commerce: A Systematic Benchmark of Modeling Strategies," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(03), pages 747-791, May.
  18. Miller, Steve, 2020. "Causal forest estimation of heterogeneous and time-varying environmental policy effects," Journal of Environmental Economics and Management, Elsevier, vol. 103(C).
  19. Qingyuan Zhao & Dylan S. Small & Ashkan Ertefaie, 2022. "Selective inference for effect modification via the lasso," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 382-413, April.
  20. Shuai Chen & Lu Tian & Tianxi Cai & Menggang Yu, 2017. "A general statistical framework for subgroup identification and comparative treatment scoring," Biometrics, The International Biometric Society, vol. 73(4), pages 1199-1209, December.
  21. Pons Rotger, Gabriel & Rosholm, Michael, 2020. "The Role of Beliefs in Long Sickness Absence: Experimental Evidence from a Psychological Intervention," IZA Discussion Papers 13582, Institute of Labor Economics (IZA).
  22. Nora Bearth & Michael Lechner, 2024. "Causal Machine Learning for Moderation Effects," Papers 2401.08290, arXiv.org, revised Apr 2024.
  23. Susan Athey & Guido Imbens, 2016. "The Econometrics of Randomized Experiments," Papers 1607.00698, arXiv.org.
  24. Gubela, Robin & Bequé, Artem & Gebert, Fabian & Lessmann, Stefan, 2018. "Conversion uplift in e-commerce: A systematic benchmark of modeling strategies," IRTG 1792 Discussion Papers 2018-062, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
  25. Zhang, Yuyang & Schnell, Patrick & Song, Chi & Huang, Bin & Lu, Bo, 2021. "Subgroup causal effect identification and estimation via matching tree," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
  26. Yizhe Xu & Tom H. Greene & Adam P. Bress & Brian C. Sauer & Brandon K. Bellows & Yue Zhang & William S. Weintraub & Andrew E. Moran & Jincheng Shen, 2022. "Estimating the optimal individualized treatment rule from a cost‐effectiveness perspective," Biometrics, The International Biometric Society, vol. 78(1), pages 337-351, March.
  27. Gubela, Robin M. & Lessmann, Stefan & Jaroszewicz, Szymon, 2020. "Response transformation and profit decomposition for revenue uplift modeling," European Journal of Operational Research, Elsevier, vol. 283(2), pages 647-661.
  28. Ruoqing Zhu & Ying-Qi Zhao & Guanhua Chen & Shuangge Ma & Hongyu Zhao, 2017. "Greedy outcome weighted tree learning of optimal personalized treatment rules," Biometrics, The International Biometric Society, vol. 73(2), pages 391-400, June.
  29. Muxuan Liang & Menggang Yu, 2023. "Relative contrast estimation and inference for treatment recommendation," Biometrics, The International Biometric Society, vol. 79(4), pages 2920-2932, December.
  30. Emily L. Butler & Eric B. Laber & Sonia M. Davis & Michael R. Kosorok, 2018. "Incorporating Patient Preferences into Estimation of Optimal Individualized Treatment Rules," Biometrics, The International Biometric Society, vol. 74(1), pages 18-26, March.
  31. Baqun Zhang & Min Zhang, 2018. "C‐learning: A new classification framework to estimate optimal dynamic treatment regimes," Biometrics, The International Biometric Society, vol. 74(3), pages 891-899, September.
  32. Adam Ciarleglio & Eva Petkova & Todd Ogden & Thaddeus Tarpey, 2018. "Constructing treatment decision rules based on scalar and functional predictors when moderators of treatment effect are unknown," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1331-1356, November.
  33. Hyung Park & Eva Petkova & Thaddeus Tarpey & R. Todd Ogden, 2021. "A constrained single‐index regression for estimating interactions between a treatment and covariates," Biometrics, The International Biometric Society, vol. 77(2), pages 506-518, June.
  34. Eoghan O'Neill & Melvyn Weeks, 2018. "Causal Tree Estimation of Heterogeneous Household Response to Time-Of-Use Electricity Pricing Schemes," Papers 1810.09179, arXiv.org, revised Oct 2019.
  35. Riccardo Di Francesco, 2022. "Aggregation Trees," CEIS Research Paper 546, Tor Vergata University, CEIS, revised 20 Nov 2023.
  36. Edward McFowland III & Sriram Somanchi & Daniel B. Neill, 2018. "Efficient Discovery of Heterogeneous Quantile Treatment Effects in Randomized Experiments via Anomalous Pattern Detection," Papers 1803.09159, arXiv.org, revised May 2023.
  37. Semenova, Daria & Temirkaeva, Maria, 2019. "The Comparison of Methods for IndividualTreatment Effect Detection," MPRA Paper 97309, University Library of Munich, Germany, revised 23 Sep 2019.
  38. Kushal S. Shah & Haoda Fu & Michael R. Kosorok, 2023. "Stabilized direct learning for efficient estimation of individualized treatment rules," Biometrics, The International Biometric Society, vol. 79(4), pages 2843-2856, December.
  39. 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.
  40. Guilloux, Agathe & Lemler, Sarah & Taupin, Marie-Luce, 2016. "Adaptive kernel estimation of the baseline function in the Cox model with high-dimensional covariates," Journal of Multivariate Analysis, Elsevier, vol. 148(C), pages 141-159.
  41. Yizhe Xu & Tom H. Greene & Adam P. Bress & Brandon K. Bellows & Yue Zhang & Zugui Zhang & Paul Kolm & William S. Weintraub & Andrew S. Moran & Jincheng Shen, 2022. "An Efficient Approach for Optimizing the Cost-effective Individualized Treatment Rule Using Conditional Random Forest," Papers 2204.10971, arXiv.org.
  42. Michael Lechner & Jana Mareckova, 2022. "Modified Causal Forest," Papers 2209.03744, arXiv.org.
  43. 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.
  44. Guihua Wang & Jun Li & Wallace J. Hopp, 2022. "An Instrumental Variable Forest Approach for Detecting Heterogeneous Treatment Effects in Observational Studies," Management Science, INFORMS, vol. 68(5), pages 3399-3418, May.
  45. 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.
  46. O'Neill, E. & Weeks, M., 2018. "Causal Tree Estimation of Heterogeneous Household Response to Time-Of-Use Electricity Pricing Schemes," Cambridge Working Papers in Economics 1865, Faculty of Economics, University of Cambridge.
  47. Shigeyuki Matsui & Hisashi Noma & Pingping Qu & Yoshio Sakai & Kota Matsui & Christoph Heuck & John Crowley, 2018. "Multi†subgroup gene screening using semi†parametric hierarchical mixture models and the optimal discovery procedure: Application to a randomized clinical trial in multiple myeloma," Biometrics, The International Biometric Society, vol. 74(1), pages 313-320, March.
  48. Gabriel Okasa, 2022. "Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance," Papers 2201.12692, arXiv.org.
  49. Susan Athey & Guido Imbens, 2015. "Recursive Partitioning for Heterogeneous Causal Effects," Papers 1504.01132, arXiv.org, revised Dec 2015.
  50. Hyung Park & Eva Petkova & Thaddeus Tarpey & R. Todd Ogden, 2023. "Functional additive models for optimizing individualized treatment rules," Biometrics, The International Biometric Society, vol. 79(1), pages 113-126, March.
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