An introduction to propensity score methods for reducing. The why, when, and how of propensity score methods for estimating causal e. Propensity score matching methods for nonexperimental. Propensity score matching psm has become a popular approach to es. Propensity scorematching methods for nonexperimental causal studies rajeev h. We propose the use of propensity score matching methods, and implement them using data from the nsw experiment. Randomization, matching, and propensity scores in the design. This course will cover some of the most popular techniques for estimating causal effects with observational data. Interested readers can consult guo and fraser 2010 and references cited therein for more information regarding variations of matching algorithms, sensitivity analyses, and other issues related to psm. In nonexperimental studies, one often collects data on many potential oonfoun ders. Heckman 1997 also played a role in the development of propensity score matching methods.
Propensity score matching is employed to create a control group that is comparable to the treated group with respect to the observed covariates. Causal case study methods is an insightful volume that brings together a comprehensive survey of the epistemological and ontological foundations of casestudy methods with a set of guidelines for the design and execution of these studies. Methods to conduct propensity score matching using sas are described in chapter 3 of faries, leon, maria haro, and obenchain 2010. Propensity scores and causal inference learning methods.
Propensity score matching methods linkedin slideshare. Estimation of causal effects with multiple treatments. Dehejiaand sadek wahba this article uses propensity score methods to estimate the treatment impact of the national supported work nsw demonstration, a labor training program, on postintervention earnings. Labor studies this paper considers causal inference and sample selection bias in nonexperimental settings in which. An application to data on right heart catheterization keisuke hirano department of economics, university of miami, po box 248126, coral gables, fl 331246550 email. Ex post facto research nonexperimental designs that are used to investigate causal relationships they examine whether one or more preexisting conditions could possibly have caused subsequent differences in groups of subjects researchers attempt to discover whether differences between groups have. Following lalonde 1986, we pair the experimental treated units with nonexperimental comparison units from the cps and psid, and compare the estimates of the treatment effect obtained using our methods to the. Intuitively,thisassumesthat,conditioningonobservable covariates,wecantakeassignmenttotreatmenttohavebeen randomandthat,inparticular,unobservablesplaynorolein. Propensity scores can also be used to weight observations like a sample. The propensity score is a common tool for estimating the causal e. Jun 08, 2011 the reader is referred to gu and rosenbaum for an indepth comparison of different matching methods. Eric ej788243 using full matching to estimate causal. Estimation of causal effects using propensity score weighting.
The authors discuss how randomized experiments allow us to assess causal effects and then turn to observational studies. Ps methods allow for estimation of the marginal treatment eect. This is all the more troubling because, although these theories require exact matching, most matching applications resort to some form of ex post strati. Pdf propensity score matching methods for nonexperimental. Onetoone nearest neighbor and caliper matching without replacement. Dehejia department of economics columbia university 420 w. Application of propensity score models in observational studies. Propensity scorebased methods for causal inference in. Scorea age schoolblackhispanic no degreemarriedre74re75 u74 u75 treatment effect means regression treatment effect nsw 185 0. We used datasets from 4 largescale cardiovascular observational studies prometheus, adaptdes. Probability of receiving the treatment given the observed covariates. Criticized by lalonde, defended by dehejia and wahba and critisized again by king and nielsen. In fact, precisely because nonexperimental studies do not have the bene. Causal case study methods university of michigan press.
Research on matching methods for causal inference in. National bureau of economic research, 1998 ocolc891671763. Cross validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. In many academic settings teaching a particular topic is applied to every student enrolled in the same academic year, it is a difficult task for researchers to design a randomized control group study. Propensity score matching psm is the most widely used matching technique for nonexperimental causal studies dehejia and wahba 2002. The estimation of causal effects is a central goal of social science research. Presentation in the international methods colloquium. Nov 11, 2014 hua he, jun hu and jiang he, overview of propensity score methods, statistical causal inferences and their applications in public health research, 10. A theory of statistical inference for matching methods in applied causal research stefano m. They lay out the assumptions needed for causal inference and describe the leading analysis methods, including matching, propensityscore methods, and instrumental variables. Causalcomparative studies involve comparison, whereas correlational studies involve relationship.
Propensity scorematching methods for nonexperimental causal studies. Comparison of propensity score methods and covariate. Applyingthe ps thereare in generalthreedifferent methods. The item propensity score matching methods for nonexperimental causal studies represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in university of manitoba libraries. Propensity score matching methods for nonexperimental causal studies rajeev h. What are the best empirical studies comparing causal. Causalcomparative studies attempt to establish causeeffect relationships. Application of propensity score models in observational studies nikki carroll, kaiser permanente colorado. A general multivariate matching method for achieving balance in observational studies. Dehejia and sadek wahba abstract this paper considers causalinference and sample selection bias in nonexperimental settings in which i few units in the nonexperimental comparison group are comparable to the treatment units, and ii selecting. Methods for causal inference with observational data. Propensity scorebased methods for causal inference in observational studies with nonbinary treatments shandong zhao1, david a van dyk2 and kosuke imai3 abstract propensity score methods are a part of the standard toolkit for applied researchers who wish to ascertain causal effects from observational data. Propensity score matching in observational studies author.
Propensity score matching is used for make causal inferences in observational studies. Nonexperimental causal studies with incomplete matching and. In this project, we develop matching methods that can help empirical researchers conduct reliable and efficient causal inference in both experimental and observational studies. Propensity score methods are increasingly being used to address this bias. Motivation for propensity score weighting propensity score methods are used to remove the effects of observable confounders when estimating the effect of a treatment on an outcome have been discussing matching methods stratification, nearest neighbor, etc. This paper considers causal inference and sample selection bias in nonexperimental settings in which. Iacusy gary kingz giuseppe porrox november 22, 2015 abstract to reduce model dependence and bias in causal inference, researchers usually use matching as a data preprocessing step, after which they apply whatever statistical. In observational studies, the true propensity score is not, in general, known. Abstract in a randomized study, subjects are randomly assigned to either a treated group or a control group. This paper applies propensity score methods, which have been developed in the statistics literature, to lalondes dataset. One great way to learn about the strengths and weaknesses of different statistical techniques for causal inference is to compare them on the same data.
The central role of propensity score in observational studies for causal effects. The validity of inference will then strictly depend on the plausibility of the assumptions underlying the employed statistical techniques. Comments on a critical appraisal of propensityscore matching in the medical literature between 1996 and 2003 by peter austin. This article focuses on common methods for matching. Using the propensity score method to estimate causal effects. Testing causal effects in observational survival data. Propensity score methods have theoretical advantages over conventional covariate adjustment, but their relative performance in realword scenarios is poorly characterized. T2 examining the relationship between adolescent marijuana use and adult outcomes. Application of propensity score models in observational. The reader is referred to gu and rosenbaum for an indepth comparison of different matching methods. Reevaluating the evaluation of training programs rajeev h.
Dehejia and sadek wahba this article uses propensity score methods to estimate the treatment impact of the national supported work nsw demonstration, a labor training program, on postintervention earnings. An application of kernelbased versus onetoone propensity score matching for a nonexperimental causal study. Following lalonde 1986, we pair the experimental treated units with nonexperimental comparison units from the cps and psid, and compare the estimates of the treatment effect obtained using our methods to the benchmark results from. The essential role of balance tests in propensitymatched observational studies. Matching methods have a few key advantages over those other approaches. As compared to multivariate matching, propensity score matching eliminates the problem of finding an area of common support for all covariates. Propensity score methods for causal inference with the. Nearest neighbor oneone or manyone matching, radius matching, kernel matching, mahalanobis metric matching, matching with a genetic algorithm. Iacusy gary kingz giuseppe porrox november 22, 2015 abstract to reduce model dependence and bias in causal inference, researchers usually use. Dehejia and sadek wahba abstractthis paper considers causal inference and sample selection bias in nonexperimental settings in which i few units in the nonexperimental comparison group are comparable to the treatment units, and ii selecting. View dehejia2002 from stat 6022 at university of cincinnati. When estimating causal effects using observational data, it is desirable to replicate a randomized experiment as closely as possible by obtaining treated and control groups with similar covariate distributions.
Rubin m any studies in social science that aim to estimate the effect of an intervention suffer from treatment selection bias, where the units who receive the treatment may have different characteristics from those in the control. However, when a study s sample size is not large, a. Propensity scorematching methods for nonexperimental. Using the propensity score method to estimate causal. People attempt to draw causal inferences using many different statistical techniques e. Section 2 presents the theory and motivation behind propensity scores, discussing how they are a crucial tool in the use of matching methods. The most common implementation of propensityscore matching is 1.
After propensity score adjustment, the distribution of baseline covariates will be balanced between treated and. Combining groupbased trajectory modeling and propensity. Affinely invariant matching methods, such as propensity score or. The effect of treatment may be estimated by directly comparing outcomes between treated and untreated subjects in the matched. We propose a strategy to test for survival function differences based on the matching design and explore sensitivity of the p. Propensity scorematching methods for nonexperimental causal. Alternatives to matching methods include adjusting for background variables in a regression model, instrumental variables, structural equation modeling, or selection models.
The central role of the propensity score in observational. A key assumption in nonexperimental studies is that of strongly ignorable. All four propensity score techniques weighting, nearest neighbor matching, optimal matching, and subclassification produced nearly identical estimates, all providing no evidence for a causal effect range of estimate. Summary and discussion of \the central role of the propensity score in observational studies for causal e ects statistics journal club, 36825 jessica chemali and michael vespe 1 summary 1.
As starr et a note, one then hew the dilemma that both. Propensity score matching developed as part of rubin causal model wikipedia contributors 2016. Outline estimating causal e ects society for prevention. We discuss the use of propensity scorematching methods, and implement them using data from the national supported work experiment. Propensity score matching methods for nonexperimental causal studies. Causal inference for statistics, social, and biomedical. We first provide an introduction to the goal of matching and the history of these methods section 1. Propensityscore matching is used frequently in the medical literature 57. Can do subsequent regression adjustment to eliminate residual imbalance in prognostically important covariates after. Using the propensity score the authors 2012 method to. Also, there are few methods addressing the hidden bias in causal survival analysis. We discuss the use of propensity score matching methods, and implement them using data from the national supported work experiment. Propensity score estimation with boosted regression for evaluating causal effects in observational studies.
T1 using full matching to estimate causal effects in nonexperimental studies. An introduction to propensity score methods for reducing the. Dehejia and sadek wahba abstractthis paper considers. A theory of statistical inference for matching methods in. 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. Propensityscore methodsfor causalinferencewith the psmatch. Propensity score matching methods for nonexperimental causal studies article in ssrn electronic journal january 2002 with 102 reads how we measure reads. Estimation of causal effects using propensity score. Propensity scores ps are an increasingly popular method to adjust for confounding in observational studies.
Application of propensity score models in observational studies nikki carroll, kaiser permanente colorado abstract treatment effects from observational studies may be biased as patients are not randomly allocated to a treatment group. Propensity score matching can be conducted using a variety of statistical packages. He focused on selection bias, with a primary emphasis on making casual inferences when there is non random assignment. The authors have written an important book, one that is accessible to students and provocatively argued.
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