Machine learning for propensity score matching and weighting : comparing different estimation techniques and assessing ifferent balance diagnostics
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[1] Manuel Wiesenfarth,et al. The Finite Sample Performance of Semi- and Nonparametric Estimators for Treatment Effects and Policy Evaluation , 2017, Comput. Stat. Data Anal..
[2] P. Austin,et al. Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies , 2010, Pharmaceutical statistics.
[3] Kevin J. Anstrom,et al. Using Inverse Probability-Weighted Estimators in Comparative Effectiveness Analyses With Observational Databases , 2007, Medical care.
[4] D. Rubin. Using Propensity Scores to Help Design Observational Studies: Application to the Tobacco Litigation , 2001, Health Services and Outcomes Research Methodology.
[5] D. McCaffrey,et al. Propensity score estimation with boosted regression for evaluating causal effects in observational studies. , 2004, Psychological methods.
[6] R. D'Agostino. Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. , 2005, Statistics in medicine.
[7] Peter C. Austin,et al. The Relative Ability of Different Propensity Score Methods to Balance Measured Covariates Between Treated and Untreated Subjects in Observational Studies , 2009, Medical decision making : an international journal of the Society for Medical Decision Making.
[8] P. Austin. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples , 2009, Statistics in medicine.
[9] Susan Athey,et al. The State of Applied Econometrics - Causality and Policy Evaluation , 2016, 1607.00699.
[10] D. Rubin,et al. The central role of the propensity score in observational studies for causal effects , 1983 .
[11] David A. Lane. Randomization Analysis of Experimental Data: The Fisher Randomization Test Comment , 1980 .
[12] Andy Liaw,et al. Classification and Regression by randomForest , 2007 .
[13] Matías Busso,et al. New Evidence on the Finite Sample Properties of Propensity Score Reweighting and Matching Estimators , 2014, Review of Economics and Statistics.
[14] Elizabeth A Stuart,et al. Improving propensity score weighting using machine learning , 2010, Statistics in medicine.
[15] W. G. Cochran. The effectiveness of adjustment by subclassification in removing bias in observational studies. , 1968, Biometrics.
[16] D. Rubin,et al. Reducing Bias in Observational Studies Using Subclassification on the Propensity Score , 1984 .
[17] E. Stuart,et al. Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies , 2015, Statistics in medicine.
[18] E. Dettmann,et al. Distance functions for matching in small samples , 2011, Comput. Stat. Data Anal..
[19] Leo Lebanov,et al. Random Forests machine learning applied to gas chromatography - Mass spectrometry derived average mass spectrum data sets for classification and characterisation of essential oils. , 2020, Talanta.
[20] Gary King,et al. Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference , 2007, Political Analysis.
[21] Harold I Feldman,et al. Model Selection, Confounder Control, and Marginal Structural Models , 2004 .
[22] D. Rubin,et al. Causal Inference for Statistics, Social, and Biomedical Sciences: Sensitivity Analysis and Bounds , 2015 .
[23] Massimo Cannas,et al. Propensity score matching with clustered data. An application to the estimation of the impact of caesarean section on the Apgar score , 2016, Statistics in medicine.
[24] P D Cleary,et al. Validating recommendations for coronary angiography following acute myocardial infarction in the elderly: a matched analysis using propensity scores. , 2001, Journal of clinical epidemiology.
[25] C. Glymour,et al. STATISTICS AND CAUSAL INFERENCE , 1985 .
[26] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .
[27] Gi-Soo Kim,et al. Causal inference with observational data under cluster-specific non-ignorable assignment mechanism , 2017, Comput. Stat. Data Anal..
[28] J. Sekhon,et al. Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies , 2006, Review of Economics and Statistics.
[29] D. Rubin,et al. Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score , 1985 .
[30] 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.
[31] S. Schneeweiss,et al. Evaluating uses of data mining techniques in propensity score estimation: a simulation study , 2008, Pharmacoepidemiology and drug safety.
[32] Donald B Rubin,et al. On principles for modeling propensity scores in medical research , 2004, Pharmacoepidemiology and drug safety.
[33] Michael I. Jordan,et al. On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes , 2001, NIPS.
[34] Jared K Lunceford,et al. Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. , 2017, Statistics in medicine.
[35] Yoshua Bengio,et al. Pattern Recognition and Neural Networks , 1995 .
[36] R. Horwitz. The planning of observational studies of human populations , 1979 .
[37] Jasjeet S. Sekhon,et al. Multivariate and Propensity Score Matching Software with Automated Balance Optimization: The Matching Package for R , 2008 .
[38] Leo Breiman,et al. Classification and Regression Trees , 1984 .