Quantifying Heterogeneous Causal Treatment Effects in World Bank Development Finance Projects
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[1] C. Blumberg. Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction , 2016 .
[2] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[3] Susan Athey,et al. Recursive partitioning for heterogeneous causal effects , 2015, Proceedings of the National Academy of Sciences.
[4] Gérard Biau,et al. Analysis of a Random Forests Model , 2010, J. Mach. Learn. Res..
[5] G. Imbens,et al. Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score , 2000 .
[6] Matt Taddy,et al. Heterogeneous Treatment Effects in Digital Experimentation , 2014 .
[7] Xiaogang Su,et al. Subgroup Analysis via Recursive Partitioning , 2009 .
[8] B. Shepherd,et al. GUIDO IMBENS, DONALD RUBIN, Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. New York: Cambridge University Press. , 2016, Biometrics.
[9] Stefan Wager,et al. Estimation and Inference of Heterogeneous Treatment Effects using Random Forests , 2015, Journal of the American Statistical Association.
[10] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[11] Nicolai Meinshausen,et al. Quantile Regression Forests , 2006, J. Mach. Learn. Res..
[12] Misha Denil,et al. Narrowing the Gap: Random Forests In Theory and In Practice , 2013, ICML.
[13] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[14] Trevor J. Hastie,et al. Confidence intervals for random forests: the jackknife and the infinitesimal jackknife , 2013, J. Mach. Learn. Res..