Propensity score matching is a common technique used to estimate the effects of a treatment or program when you dont have a randomized controlled experiment. Propensity score matching, differenceindifferences models, treatment evaluation in stata. This type of matching is known as propensityscore matching psm. One example of a causal inference that researchers might try to determine is whether a specific management practice, such as group training or a stock option plan, increases organizational performance. Guidance, stata code, and empirical examples are given to illustrate 1 the process. Pdf programme evaluation using difference in difference.
The mnps function, which stands for multinomial propensity scores, estimates propensity score weights for studies involving more than two treatment or exposure groups. However, stata introduced a new teffects command for estimating treatments effects in a variety of ways, including propensity score matching. The stata twang commands were developed to support the use of the twang tools without requiring analysts to learn r. We addressed this issue by using a propensityscore matching procedure that enabled us to match early and late. Propensity models depend on the potential outcomes model popularized by don rubin1. How to extend the propensity score methods to multilevel data. We will specify the common support option so that it. Propensity scores for the estimation of average treatment effects in. This number of blocks ensures that the mean propensity score is not different for treated and controls in each blocks step 2. We addressed this issue by using a propensity score matching procedure that enabled us. Implementing propensity score matching estimators with stata. Stepbystep guidelines for propensity score weighting. An intuitive introduction to propensity score matching youtube. Propensity score matching is a way to correct the estimation of treatment e.
To model the steps involved in preparing for and carrying out propensity score analyses by providing stepbystep guidance and stata code applied to an empirical dataset. Psm attempts to reduce the bias due to confounding variables that could. Rosenbaum and rubin 1983, the matching is often conducted based on the probability of being assigned into the intervention, which is called the propensity score. Apr 06, 2018 stata has a function for tseffects for obtaining propensity scores, and the function of psmatch for propensity score matching. We will specify the logit option, otherwise it would use probit which is the default. Why propensity scores should not be used for matching gary kingy richard nielsenz november 10, 2018 abstract we show that propensity score matching psm, an enormously popular method of preprocessing data for causal inference, often accomplishes the opposite of its intended goal thus increasing imbalance, inef. Propensity score matching is used when a group of subjects receive a treatment and wed like to compare their outcomes with the outcomes of a control group. Apr 30, 2014 to model the steps involved in preparing for and carrying out propensity score analyses by providing stepbystep guidance and stata code applied to an empirical dataset. Probability of assignment to a 10% relative to a 5% rate increase. Propensity score methods are not a panacea for the selfselection problem.
An intuitive introduction to propensity score matching. Test of balancing property of the propensity score use option detail if you want more detailed output variable w3firstsex is not balanced in block 1. The stata commands to do this are logistic t x1 x2 x3. A practical guide to getting started with propensity scores. The trainers will present the theory behind the method, how it differs from other analyses of statistical control, and provide an illustration of propensity score matching. The most commonly used platform for these tools is stata and we will present the.
This type of matching is known as propensity score matching psm. Propensity score matching regression discontinuity limited. Statistics treatment effects matching estimators propensityscore matching. Some practical guidance for the implementation of propensity. Why propensity scores should not be used for matching. Implementing propensity score matching estimators with stata preparing the dataset keep only one observation per individual estimate the propensity score on the xs e. Favored not for the overall estimate as much as the substantive value. Brief intro to propensity score matching psm for estimation of causal effects in observational studies. Propensity scores for the estimation of average treatment. Matching most popular propensity score based method we match subjects from the treatment groups by ex subjects who are unable to be matched are discarded from the analysis a. You can also run postestimation regression with the functions. Whether and if true how to incorporate multilevel structure into the modeling for propensity score. Multivariate and propensity score matching software with automated balance optimization. Psm does not need bias correction, because psm matches on a single continuous covariate.
Propensity score matching in observational studies propensity scores are an alternative method to estimate the effect of receiving treatment when random assignment of treatments to subjects is not feasible. Sekhon uc berkeley abstract matching is an r package which provides functions for multivariate and propensity score matching and for nding optimal covariate balance based on a genetic search algorithm. Stepbystep guidelines for propensity score weighting with. Propensity scorematching methods, as we demonstrate, are especially useful under such circumstances because they provide a natural weighting scheme that yields unbiased estimates of the treatment impact. Propensity score matching in observational studies author. The articles appearing in the stata journal may be copied or reproduced as printed copies, in whole or in part, as long as any copy or reproduction includes attribution to both 1 the author and 2 the stata journal. For more information about propensity score weighting, see the section propensity score weighting on page 7707. Propensity score estimation is a pure prediction problem.
Propensity score matching allows one to estimate the att imbens, 2004. Spanish stata meeting 2018 outline brief intro to propensity score matching psm for estimation of causal effects in observational studies psm with clustered multilevel, hierarchical data psm in stata available routines how to implement psm with clustered data dofile and dataset to replicate the analyses in these slides can be. With this case study in hand, you will feel confident that you have the tools necessary to begin answering some of your own research questions using propensity scores. We will specify the common support option so that it will leave out cases that lie outside the range of.
In addition, we also investigate the impact of assigning different weights to the propensity score in the mahalanobis metric, namely 0. In contrast, the nearestneighbor matching estimator implemented in teffects nnmatch uses a bias. Propensity score matching with xtreg regressions statalist. Propensity score methods for multilevel data propensity score has been developed and applied in crosssectional settings single level data. Briggs university of colorado may 20, 2009 abstract using observational data from the education longitudinal survey of 2002, the effect of coaching on the sat is. However, when the sampling design oversamples treated units, it has been found that matching on the log odds of the propensity score p1 p is a superior criterion. An introduction to propensity score methods for reducing. Pr t 1 s e ts can estimate the effect of an event on those who do and do not experience it in the observational data through matching. Jun 08, 2011 propensity score matching allows one to estimate the att imbens, 2004. For r fans, here is a nice tutorial on propensity score matching. There are three ways to use the propensity score to do this balancing. But transformed outcome introduces variance due to weighting factor.
Stata has a function for tseffects for obtaining propensity scores, and the function of psmatch for propensity score matching. They suffer systematic bias in cases where standard regression methods do. Matching estimator and transformed outcome both unbiased in large sample when perfect matching can be found. Propensity score matching psm has become a popular approach to estimate causal treatment effects. Treatment evaluation is the estimation of the average effect of a program or treatment on the outcome of interest. In the statistical analysis of observational data, propensity score matching psm is a statistical matching technique that attempts to estimate the effect of a treatment, policy, or other intervention by accounting for the covariates that predict receiving the treatment. Propensity score matching stata program and output. Propensity score matching propensity score estimation 6 matching strategy and att estimation propensity score matching with stata nearest neighbor matching example. Propensity score matching methods, as we demonstrate, are especially useful under such circumstances because they provide a natural weighting scheme that yields unbiased estimates of the treatment impact. Matching now we will match patients in each treatment on propensity score using the psmatch2 command in stata. Outline 1 observational studies and propensity score 2 motivating example. And now is a good time to look at the distribution of your propensity score. Propensity score matching with clustered data in stata.
The psmatch procedure optionally creates strata of observations that have similar propensity scores. Some practical guidance for the implementation of propensity score. Recently, the spotlight has focused on one method, propensity score matching psm, as the. Over the past 25 years, evaluators of social programs have searched for nonexperimental methods that can substitute effectively for experimental ones. Introduction methods such as nearest neighbor matching or propensity score techniques have become popular in the social sciences in recent years to preprocess data prior to the estimation of causal e ects in observational studies with binary treatments under the selection on observables. Is taking math lessons after school helpful in improving score. Propensity score matching basic mechanics of matching the matching criterion could be as simple as the absolute difference in the propensity score for treated vs. Using linear regression and propensity score matching to estimate the effect of coaching on the sat ben domingue derek c. Quasiexperimentelle wirkungsevaluation mit propensity score matching.
Propensity score matching psm refers to the pairing of treatment and control units with similar values on. Quasiexperimentelle wirkungsevaluation mit propensity score. For more information, see the section propensity score strati. Multivariate and propensity score matching software with. We can estimate propensity score using logistic regression. Bin the sample into quintiles or finer by propensity score. Matching cases on propensity score will approximately balance treated and untreated. End of the estimation with the nearest neighbor matching random draw method. The most common implementation of propensity score matching is onetoone or pair matching, in which pairs of treated and untreated subjects are formed, such that matched subjects have similar values of the propensity score. Five subclasses are expected to remove 90% of bias from modeled covariates.
Methods for constructing and assessing propensity scores. An introduction to propensity score methods for reducing the. Jul 02, 2018 a pdf of the slides presented can be found here. Using the propensity score the authors 2012 method to. Propensity score matching psm has become a popular approach to estimate. The key contribution of this paper is to discuss and apply propensity scorematching methods, which are new to the economics literature. Tips for clinical researchers lecture series that is sponsored by the irving. Propensity score matching and related models examples in stata greedy matching and subsequent analysis of hazard rates optimal matching postfull matching analysis using the hodgeslehmann aligned rank test postpair matching analysis using regression. For many years, the standard tool for propensity score matching in stata has been the psmatch2 command, written by edwin leuven and barbara sianesi. Lecture notes on propensity score matching jinlung lin this lecture note is intended solely for teaching. Practically, matching methods seem to work better than regression and weighting methods in some.
A comparison of outcomes is made between treated and control groups. We use logistic regression to calculate the propensity scores. Pdf quasiexperimentelle wirkungsevaluation mit propensity. The key contribution of this paper is to discuss and apply propensity score matching methods, which are new to the economics literature. Matching in stata psmatch2 package created by edwin leuven and barbara sianesi. Propensity score matching and related models examples in stata greedy matching and subsequent analysis of hazard rates optimal matching postfull matching analysis using the hodgeslehmann aligned rank test postpair matching analysis using regression of difference scores propensity score weighting. It is widely applied when evaluating labour market policies, but empirical examples can be found in very diverse fields of study. Propensity score matching psm uses a vector of observed variables to predict the probability of experiencing the event participation to create a counterfactual group pt. Using linear regression and propensity score matching. Nonparametric preprocessing for parametric causal inference1 daniel e. Mar 28, 2015 propensity score matching is a common technique used to estimate the effects of a treatment or program when you dont have a randomized controlled experiment. The teffects psmatch command has one very important.