Machine learning for propensity score matching and weighting : comparing different estimation techniques and assessing ifferent balance diagnostics

[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 .