Assessment of heterogeneous treatment effect estimation accuracy via matching
暂无分享,去创建一个
[1] D. Green,et al. Modeling Heterogeneous Treatment Effects in Survey Experiments with Bayesian Additive Regression Trees , 2012 .
[2] Vivian C. Wong,et al. Three conditions under which experiments and observational studies produce comparable causal estimates: New findings from within‐study comparisons , 2008 .
[3] James Bennett,et al. The Netflix Prize , 2007 .
[4] Yen-Liang Chen,et al. The minimal average cost flow problem , 1995 .
[5] Trevor Hastie,et al. Some methods for heterogeneous treatment effect estimation in high dimensions , 2017, Statistics in medicine.
[6] D. Rubin,et al. Causal Inference for Statistics, Social, and Biomedical Sciences: A General Method for Estimating Sampling Variances for Standard Estimators for Average Causal Effects , 2015 .
[7] D. Rubin,et al. Reducing Bias in Observational Studies Using Subclassification on the Propensity Score , 1984 .
[8] Janet S Twyman,et al. Handbook on Personalized Learning for States, Districts, and Schools. , 2016 .
[9] T. Speed,et al. On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9 , 1990 .
[10] Oswald Veblen,et al. On Matrices Whose Elements Are Integers , 1921 .
[11] Alexander Schrijver,et al. Theory of linear and integer programming , 1986, Wiley-Interscience series in discrete mathematics and optimization.
[12] D. Rubin,et al. The central role of the propensity score in observational studies for causal effects , 1983 .
[13] L. Lesko,et al. Personalized Medicine: Elusive Dream or Imminent Reality? , 2007, Clinical pharmacology and therapeutics.
[14] Yen S. Low,et al. Comparing high-dimensional confounder control methods for rapid cohort studies from electronic health records , 2015, Journal of comparative effectiveness research.
[15] Trevor Hastie,et al. Synth-Validation: Selecting the Best Causal Inference Method for a Given Dataset , 2017, 1711.00083.
[16] Stefan Wager,et al. Estimation and Inference of Heterogeneous Treatment Effects using Random Forests , 2015, Journal of the American Statistical Association.
[17] Donald B. Rubin,et al. MULTIVARIATE MATCHING METHODS THAT ARE EQUAL PERCENT BIAS REDUCING, I: SOME EXAMPLES , 1974 .
[18] D. Rubin. [On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9.] Comment: Neyman (1923) and Causal Inference in Experiments and Observational Studies , 1990 .
[19] D. Rubin. Estimating causal effects of treatments in randomized and nonrandomized studies. , 1974 .
[20] R. Lalonde. Evaluating the Econometric Evaluations of Training Programs with Experimental Data , 1984 .
[21] Marc Ratkovic,et al. Estimating treatment effect heterogeneity in randomized program evaluation , 2013, 1305.5682.
[22] D. Rubin. Bias Reduction Using Mahalanobis-Metric Matching , 1980 .
[23] Paul R. Rosenbaum,et al. Modern Algorithms for Matching in Observational Studies , 2020, Annual Review of Statistics and Its Application.
[24] Ashkan Ertefaie,et al. Variable Selection in Causal Inference using a Simultaneous Penalization Method , 2015, 1511.08501.
[25] Pieter Abbeel,et al. Transfer Learning for Estimating Causal Effects using Neural Networks , 2018, ArXiv.
[26] P. Rosenbaum. A Characterization of Optimal Designs for Observational Studies , 1991 .
[27] Susan Athey,et al. Recursive partitioning for heterogeneous causal effects , 2015, Proceedings of the National Academy of Sciences.
[28] Jennifer L. Hill,et al. Bayesian Nonparametric Modeling for Causal Inference , 2011 .
[29] A. Agresti. An introduction to categorical data analysis , 1990 .
[30] B. Hansen. The prognostic analogue of the propensity score , 2008 .
[31] P. Rosenbaum. Design of Observational Studies , 2009, Springer Series in Statistics.
[32] M. Kendall. Theoretical Statistics , 1956, Nature.
[33] Paul R. Rosenbaum,et al. Optimal Matching for Observational Studies , 1989 .
[34] Sören R. Künzel,et al. Metalearners for estimating heterogeneous treatment effects using machine learning , 2017, Proceedings of the National Academy of Sciences.
[35] Elizabeth A Stuart,et al. Matching methods for causal inference: A review and a look forward. , 2010, Statistical science : a review journal of the Institute of Mathematical Statistics.
[36] D. Rubin. Matched Sampling for Causal Effects: Matching to Remove Bias in Observational Studies , 1973 .
[37] B. Hansen. Full Matching in an Observational Study of Coaching for the SAT , 2004 .