Conformal inference of counterfactuals and individual treatment effects
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DOI: 10.1111/rssb.12445
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- J. P. Florens & J. J. Heckman & C. Meghir & E. Vytlacil, 2008.
"Identification of Treatment Effects Using Control Functions in Models With Continuous, Endogenous Treatment and Heterogeneous Effects,"
Econometrica, Econometric Society, vol. 76(5), pages 1191-1206, September.
- Jean-Pierre Florens & James J. Heckman & Costas Meghir & Edward J. Vytlacil, 2008. "Identification of Treatment Effects Using Control Functions in Models with Continuous, Endogenous Treatment and Heterogeneous Effects," NBER Working Papers 14002, National Bureau of Economic Research, Inc.
- J.P. Florensy & J. J. Heckmanz & C. Meghirx & E. Vytlacil, 2008. "Identification of Treatment Effects Using Control Functions in Models with Continuous, Endogenous Treatment and Heterogeneous Effects," Working Papers 200832, Geary Institute, University College Dublin.
- Imai, Kosuke & Strauss, Aaron, 2011. "Estimation of Heterogeneous Treatment Effects from Randomized Experiments, with Application to the Optimal Planning of the Get-Out-the-Vote Campaign," Political Analysis, Cambridge University Press, vol. 19(1), pages 1-19, January.
- Roger Koenker, 2017. "Quantile Regression: 40 Years On," Annual Review of Economics, Annual Reviews, vol. 9(1), pages 155-176, September.
- Bradley Efron, 2014. "Estimation and Accuracy After Model Selection," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 991-1007, September.
- Grimmer, Justin & Messing, Solomon & Westwood, Sean J., 2017. "Estimating Heterogeneous Treatment Effects and the Effects of Heterogeneous Treatments with Ensemble Methods," Political Analysis, Cambridge University Press, vol. 25(4), pages 413-434, October.
- Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003.
"Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score,"
Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
- Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2000. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," NBER Technical Working Papers 0251, National Bureau of Economic Research, Inc.
- Guido Imbens, 2000. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometric Society World Congress 2000 Contributed Papers 1166, Econometric Society.
- Jing Lei & Max G’Sell & Alessandro Rinaldo & Ryan J. Tibshirani & Larry Wasserman, 2018. "Distribution-Free Predictive Inference for Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1094-1111, July.
- Roger Koenker, 2017. "Quantile regression 40 years on," CeMMAP working papers CWP36/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Stefan Wager & Susan Athey, 2018.
"Estimation and Inference of Heterogeneous Treatment Effects using Random Forests,"
Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
- Wager, Stefan & Athey, Susan, 2017. "Estimation and Inference of Heterogeneous Treatment Effects Using Random Forests," Research Papers 3576, Stanford University, Graduate School of Business.
- Djebbari, Habiba & Smith, Jeffrey, 2008.
"Heterogeneous impacts in PROGRESA,"
Journal of Econometrics, Elsevier, vol. 145(1-2), pages 64-80, July.
- Djebbari, Habiba & Smith, Jeffrey A., 2008. "Heterogeneous Impacts in PROGRESA," IZA Discussion Papers 3362, Institute of Labor Economics (IZA).
- Jonas Peters & Peter Bühlmann & Nicolai Meinshausen, 2016. "Causal inference by using invariant prediction: identification and confidence intervals," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(5), pages 947-1012, November.
- Elizabeth A. Stuart & Stephen R. Cole & Catherine P. Bradshaw & Philip J. Leaf, 2011. "The use of propensity scores to assess the generalizability of results from randomized trials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(2), pages 369-386, April.
- Mauricio Sadinle & Jing Lei & Larry Wasserman, 2019. "Least Ambiguous Set-Valued Classifiers With Bounded Error Levels," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(525), pages 223-234, January.
- David S. Yeager & Paul Hanselman & Gregory M. Walton & Jared S. Murray & Robert Crosnoe & Chandra Muller & Elizabeth Tipton & Barbara Schneider & Chris S. Hulleman & Cintia P. Hinojosa & David Paunesk, 2019. "A national experiment reveals where a growth mindset improves achievement," Nature, Nature, vol. 573(7774), pages 364-369, September.
- Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
- Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, November.
- Kapelner, Adam & Bleich, Justin, 2016. "bartMachine: Machine Learning with Bayesian Additive Regression Trees," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 70(i04).
- Yu, Keming & Moyeed, Rana A., 2001. "Bayesian quantile regression," Statistics & Probability Letters, Elsevier, vol. 54(4), pages 437-447, October.
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- Zhang, Yingying & Shi, Chengchun & Luo, Shikai, 2023. "Conformal off-policy prediction," LSE Research Online Documents on Economics 118250, London School of Economics and Political Science, LSE Library.
- David M. Ritzwoller & Joseph P. Romano & Azeem M. Shaikh, 2024. "Randomization Inference: Theory and Applications," Papers 2406.09521, arXiv.org.
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