Propensity score methods for causal inference: an overview

Behaviormetrika - Tập 45 - Trang 317-334 - 2018
Wei Pan1, Haiyan Bai2
1Duke University, DUMC 3322, Durham, USA
2University of Central Florida, Orlando, USA

Tóm tắt

Propensity score methods are popular and effective statistical techniques for reducing selection bias in observational data to increase the validity of causal inference based on observational studies in behavioral and social science research. Some methodologists and statisticians have raised concerns about the rationale and applicability of propensity score methods. In this review, we addressed these concerns by reviewing the development history and the assumptions of propensity score methods, followed by the fundamental techniques of and available software packages for propensity score methods. We especially discussed the issues in and debates about the use of propensity score methods. This review provides beneficial information about propensity score methods from the historical point of view and helps researchers to select appropriate propensity score methods for their observational studies.

Tài liệu tham khảo

Bai H (2015) Methodological considerations in implementing propensity score matching. In: Pan W, Bai H (eds) Propensity score analysis: fundamentals, developments, and extensions. Guilford Press, New York, pp 74–88

Hamilton MA (1979) Choosing the parameter for a 2 × 2 table or a 2 × 2 × 2 table analysis. Am J Epidemiol 109(3):362–375

Hirano K, Imbens GW (2001) Estimation of causal effects using propensity score weighting: an application to data on right heart catheterization. Health Serv Outcomes Res Method 2(3):259–278

Huesch MD (2013) External adjustment sensitivity analysis for unmeasured confounding: an application to coronary stent outcomes, Pennsylvania 2004–2008. Health Serv Res 48(3):1191–1214

Keele LJ (2015) Package ‘rbounds’, version 2.1. https://cran.r-project.org/web/packages/rbounds/rbounds.pdf. Accessed 20 Jan 2016

Leuven E, Sianesi B (2012) PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing. Statistical Software Components S432001. Boston College Department of Economics. http://ideas.repec.org/c/boc/bocode/s432001.html. Accessed 6 May 2014

Pan W, Bai H (eds) (2015a) Propensity score analysis: fundamentals and developments. Guilford Press, New York

Pan W, Bai H (2016b) Propensity score methods in nursing research: take advantage of them but proceed with caution. Nurs Res 65(6):421–424

Pattanayak CW (2015) Evaluating covariate balance. In: Pan W, Bai H (eds) Propensity score analysis: fundamentals and developments. Guilford Press, New York, pp 89–112

Robins JM, Hernan MA, Brumback B (2000a) Marginal structural models and causal inference in epidemiology. Epidemiology 11:550–560

Rosenbaum PR (2010) Observational studies, 2nd edn. Springer, New York

Rubin DB, Thomas N (1996) Matching using estimated propensity scores: relating theory to practice. Biometrics 52:249–264. https://doi.org/10.2307/2533160

SAS Institute Inc. (2017a) SAS/STAT® 14.3 user’s guide: the CAUSALTRT procedure. SAS Institute Inc., Cary, NC

SAS Institute Inc. (2017b) SAS/STAT® 14.3 user’s guide: the PSMATCH procedure. SAS Institute Inc., Cary, NC

Schuler M (2015) Overview of implementing propensity score analyses in statistical software. In: Pan W, Bai H (eds) Propensity score analysis: fundamentals and developments. Guilford Press, New York, pp 20–48

Winship C, Morgan SL (1999) The estimation of causal effects from observational data. Ann Rev Sociol 25:659–706