收藏·倾向匹配得分经典文章、书籍、命令汇总

一、文章、书籍汇总

1、《Propensity scores for the estimation of average treatment effects in observational studies》，Leonardo Grilli and Carla Rampichini，Training Sessions on Causal Inference Bristol - June 28-29, 2011

2、就业培训的处理效应评估文章，Cameron&Trived《微观计量经济学：方法与应用》（中译本，上海财经大学出版社，2010）pp794-800。陈强老师的《高级计量经济学及stata应用（第二版）》（高等教育出版社，2014）pp546-555。

3、《倾向值匹配法的概述与应用：从统计关联到因果推论》，作者：苏毓淞

4、《倾向值分析：统计方法与应用》 ，对倾向值分析的起源、原理、应用和示例做了详细的介绍，并提供了数据和软件代码（Stata）。该书为译文，英文著作已在2014年推出第版《Propensity Score Analysis: Statistical Methods and Applications 2nd Edition》，对新的方法进行了更新，内容也更丰富，应该是目前关于倾向值分析最详细的教材。第二版的数据和代码见：http://ssw.unc.edu/psa/home

5、 Randolph J J, Falbe K, Manuel A K, et al. A Step-by-StepGuide to Propensity Score Matching in R.[J]. Practical Assessment Research & Evaluation, 2014, 19.

R软件MatchIt包的简易教程，对结果的讲解较详细，可实现常用倾向值分析的功能。

6、MatchIt: Nonparametric Preprocessing for Parametric Causal Inference

R软件MatchIt包教程的详细版，对参数设置和原理进行了解释，并提供操作的语法示例。

7、 黄福强,杜春霖,孙梦辉,等. 倾向评分配比在SPSS软件上的实现[J]. 南方医科大学学报,2015,(11):1597-1601.

8、Jalan J, Ravallion M. Estimating the benefit incidence of an antipoverty program by propensity-score matching[J]. Journal of Business & Economic Statistics, 2003, 21(1): 19-30.

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二、代码合集

R

• MatchIt http://gking.harvard.edu/matchit

• Ho, D.E., Imai, K., King, G., and Stuart, E.A. (2011). MatchIt: Nonparametric preprocessing for parameteric causal inference. Journal of Statistical Software 42(8). http://www.jstatsoft.org/v42/i08

• Two-step process: does matching, then user does outcome analysis (integrated with Zelig package for R)

• Wide array of estimation procedures and matching methods available: nearest neighbor, Mahalanobis, caliper, exact, full, optimal, subclassification

• Built-in numeric and graphical diagnostics

• Matching http://sekhon.berkeley.edu/matching

• Sekhon, J. S. (2011). Multivariate and propensity score matching software with automated balance optimization: The Matching package for R. Journal of Statistical Software 42(7). http://www.jstatsoft.org/v42/i07

• Uses automated procedure to select matches, based on univariate and multivariate balance diagnostics

• Primarily 1:M matching (where M is a positive integer), allows matching with or without replacement, caliper, exact

• Includes built-in effect and variance estimation procedures

• twang http://cran.r-project.org/web/packages/twang/index.html

• Ridgeway, G., McCaffrey, D., and Morral, A. (2006). twang: Toolkit for weighting and analysis of nonequivalent groups.

• Functions for propensity score estimating and weighting, nonresponse weighting, and diagnosis of the weights

• Primarily uses generalized boosted regression to estimate the propensity scores

• Includes functionality for multiple group weighting, marginal structural models

• cem http://gking.harvard.edu/cem/

• Iacus, S.M., King, G., and Porro, G. (2008). Matching for Causal Inference Without Balance Checking. Available here.

• Implements coarsened exact matching

• Can also be implemented through MatchIt

• optmatch http://cran.r-project.org/web/packages/optmatch/index.html

• Hansen, B.B., and Fredrickson, M. (2009). optmatch: Functions for optimal matching.

• Variable ratio, optimal, and full matching

• Can also be implemented through MatchIt

• PSAgraphics http://cran.r-project.org/web/packages/PSAgraphics/index.html

• Helmreich, J.E. and Pruzek, R.M. (2009). PSAgraphics: An R Package to Support Propensity Score Analysis. Journal of Statistical Software 29(6). Available here.

• From webpage: "A collection of functions that primarily produce graphics to aid in a Propensity Score Analysis (PSA). Functions include: cat.psa and box.psa to test balance within strata of categorical and quantitative covariates, circ.psa for a representation of the estimated effect size by stratum, loess.psa that provides a graphic and loess based effect size estimate, and various balance functions that provide measures of the balance achieved via a PSA in a categorical covariate."

• Synth https://cran.r-project.org/web/packages/Synth/

• Abadie, A., Diamond, A., and Hainmueller, H. (2011). Synth: An R Package for Synthetic Control Methods in Comparative Cast Studies. Journal of Statistical Software 42(13). http://www.jstatsoft.org/v42/i13

• Implements weighting approach to creating synthetic control groups

• Useful when there is a single treated unit, such as a state or country. Main idea is to form a weighted average of comparison units that, when weighted, looks like the treated unit.

• Cobalt: Covariate balance tables and plots https://cran.r-project.org/web/packages/cobalt/index.html

• Generates balance tables and figures for covariates following matching, weighting, or subclassification

• Integrated with MatchIt, twang, matching, CBPS, and ebal

• CBPS https://cran.r-project.org/web/packages/CBPS/index.html

• Imai, K., and Ratkovic, M. (2014). Covariate balancing propensity score. Journal of the Royal Statistical Society Series B 76(1): 243-263.

• Estimates propensity score in way that automatically targets balance

• Also includes functionality for marginal structural models, three- and four-valued treatment levels, and continuous treatments

• ebal: Entropy reweighting to create balanced samples https://cran.r-project.org/web/packages/ebal/index.html

• Hainmueller, J. (2012). Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. Political Analysis 20: 25-46.

• Reweights dataset such that covariate distributions in reweighted data satisfy a set of user specified moment conditions.

Stata

• teffects suite http://www.stata.com/manuals13/te.pdf

• Stata written causal inference commands for matching and weighting

• Includes balance diagnostics, 1:1 matching, weighting, doubly robust approaches

• psmatch2 http://ideas.repec.org/c/boc/bocode/s432001.html

• Leuven, E. and Sianesi, B. (2003). psmatch2. Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing.

• Allows k:1 matching, kernel weighting, Mahalanobis matching

• Includes built-in diagnostics

• Includes procedures for estimating ATT or ATE

• pscore http://www.lrz-muenchen.de/~sobecker/pscore.html

• Becker, S.O. and Ichino, A. (2002). Estimation of average treatment effects based on propensity scores (2002) The Stata Journal 2(4): 358-377.

• k:1 matching, radius (caliper) matching, and stratification (subclassification)

• For estimating the ATT

• match http://www.economics.harvard.edu/faculty/imbens/software_imbens

• Abadie, A., Drukker, D., Herr, J. L., and Imbens, G. W. (2004). Implementing matching estimators for average treatment effects in Stata. The Stata Journal 4(3): 290-311. Available here.

• Primarily k:1 matching (with replacement)

• Allows estimation of ATT or ATE, including robust variance estimators

• cem http://gking.harvard.edu/cem/

• Iacus, S.M., King, G., and Porro, G. (2008). Matching for Causal Inference Without Balance Checking. Available here.

• Implements coarsened exact matching

SAS

• SAS usage note: http://support.sas.com/kb/30/971.html

• Local and global optimal propensity score matching

• Coca-Perraillon, M. (2007). Local and global optimal propensity score matching. In SAS Global Forum 2007. Paper 185-2007. Available here.

• Variety of matching methods. No built in diagnostics. Assumes propensity score already estimated.

• cem http://gking.harvard.edu/cem/

• Iacus, S.M., King, G., and Porro, G. (2008). Matching for Causal Inference Without Balance Checking. Available here.

• Implements coarsened exact matching

• Greedy matching (1:1 nearest neighbor)

• Parsons, L. S. (2001). Reducing bias in a propensity score matched-pair sample using greedy matching techniques. In SAS SUGI 26, Paper 214-26. Available here.

• Parsons, L.S. (2005). Using SAS software to perform a case-control match on propensity score in an observational study. In SAS SUGI 30, Paper 225-25. Available here.

• Kosanke, J., and Bergstralh, E. (2004). gmatch: Match 1 or more controls to cases using the GREEDY algorithm. http://www.mayo.edu/research/departments-divisions/department-health-sciences-research/division-biomedical-statistics-informatics/software/locally-written-sas-macros

• 1:1 Mahalanbois matching within propensity score calipers

• Feng, W.W., Jun, Y., and Xu, R. (2005). A method/macro based on propensity score and Mahalanobis distance to reduce bias in treatment comparison in observational study. www.lexjansen.com/pharmasug/2006/publichealthresearch/pr05.pdf

• Weighting

• Leslie, S. and Thiebaud, P. (2006). Using propensity scores to adjust for treatment selection bias. http://www.lexjansen.com/wuss/2006/Analytics/ANL-Leslie.pdf

• Variable ratio matching, optimal matching algorithm

• Kosanke, J., and Bergstralh, E. (2004). Match cases to controls using variable optimal matching. http://www.mayo.edu/research/departments-divisions/department-health-sciences-research/division-biomedical-statistics-informatics/software/locally-written-sas-macros

SPSS

• Propensity score matching in SPSS http://arxiv.org/ftp/arxiv/papers/1201/1201.6385.pdf

• Thoemmes, F. (2012). Propensity score matching in SPSS. http://sourceforge.net/projects/psmspss/files/

• Nearest neighbor propensity score matching with various options (with/without replacement, calipers, k to 1, etc.)

• Detailed balance statistics and graphs

• Actually calls MatchIt using a point and click interface

Software for performing analyses of sensitivity to an unobserved confounder

• R

• rbounds: An R package for sensitivity analysis with matched data (L. Keele). http://www.personal.psu.edu/ljk20/rbounds.html

• sensitivity function in twang package (G. Ridgeway et al.). http://rss.acs.unt.edu/Rdoc/library/twang/html/sensitivity.html

• Stata

• rbounds: Stata module to perform Rosenbaum sensitivity analysis for average treatment effects on the treated (M. Gangl) http://econpapers.repec.org/software/bocbocode/s438301.htm

• sensatt: A simulation-based sensitivity analysis for matching estimators (T. Nannicini) http://www.tommasonannicini.eu/Portals/0/sensatt_wp_4.pdf

• Nannicini T. (2007). A Simulation-Based Sensitivity Analysis for Matching Estimators. Stata Journal, 7(3), 334-350

• Ichino A., Mealli F., Nannicini T. (2008). From Temporary Help Jobs to Permanent Employment: What Can We Learn from Matching Estimators and their Sensitivity? Journal of Applied Econometrics, 23(3), 305-327.

• Excel

• Love, T.E. (2008) Spreadsheet-based sensitivity analysis calculations for matched samples. Center for Health Care Research & Policy, Case Western Reserve University. http://www.chrp.org/propensity/, http://www.chrp.org/propensity/sensitivitydocumentation.pdf, http://www.chrp.org/propensity/sensitivityspreadsheet.xls