Propensity scorematching methods for nonexperimental causal studies. Dehejia and sadek wahba abstractthis paper considers. Propensity score matching is used for make causal inferences in observational studies. Causalcomparative studies involve comparison, whereas correlational studies involve relationship. Onetoone nearest neighbor and caliper matching without replacement.
Previous versions of this paper were circulated under the title an oversampling algorithm for. He focused on selection bias, with a primary emphasis on making casual inferences when there is non random assignment. This paper applies propensity score methods, which have been developed in the statistics literature, to lalondes dataset. 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. An application to data on right heart catheterization keisuke hirano department of economics, university of miami, po box 248126, coral gables, fl 331246550 email. Propensity score matching methods linkedin slideshare. Propensity score methods are increasingly being used to address this bias. The effect of treatment may be estimated by directly comparing outcomes between treated and untreated subjects in the matched. Intuitively,thisassumesthat,conditioningonobservable covariates,wecantakeassignmenttotreatmenttohavebeen randomandthat,inparticular,unobservablesplaynorolein. Figure 1shows that the average math score for students who attend public schools is 3. Propensity scores ps are an increasingly popular method to adjust for confounding in observational studies. T1 using full matching to estimate causal effects in nonexperimental studies.
Methods for causal inference with observational data. Outline estimating causal e ects society for prevention. National bureau of economic research, 1998 ocolc891671763. The why, when, and how of propensity score methods for estimating causal e. Propensity score matching psm is the most widely used matching technique for nonexperimental causal studies dehejia and wahba 2002. An introduction to propensity score methods for reducing. Propensityscore methodsfor causalinferencewith the psmatch. We used datasets from 4 largescale cardiovascular observational studies prometheus, adaptdes.
Estimation of causal effects using propensity score. 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. Causal case study methods university of michigan press. 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. 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. The propensity score is a common tool for estimating the causal e. 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 is employed to create a control group that is comparable to the treated group with respect to the observed covariates. The estimation of causal effects is a central goal of social science research. We first provide an introduction to the goal of matching and the history of these methods section 1.
Ps methods allow for estimation of the marginal treatment eect. Affinely invariant matching methods, such as propensity score or. Comparison of propensity score methods and covariate. They lay out the assumptions needed for causal inference and describe the leading analysis methods, including matching, propensityscore methods, and instrumental variables. Matching methods have a few key advantages over those other approaches. In nonexperimental studies, one often collects data on many potential oonfoun ders. T2 examining the relationship between adolescent marijuana use and adult outcomes. Using the propensity score method to estimate causal.
A theory of statistical inference for matching methods in. Propensity scorematching methods for nonexperimental. The central role of propensity score in observational studies for causal effects. The authors discuss how randomized experiments allow us to assess causal effects and then turn to observational studies. People attempt to draw causal inferences using many different statistical techniques e.
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. In contrast with lalondes findings, using propensity score methods, we are able closely to replicate the experimental training effect. The reader is referred to gu and rosenbaum for an indepth comparison of different matching methods. Jun 08, 2011 the reader is referred to gu and rosenbaum for an indepth comparison of different matching methods. We propose a strategy to test for survival function differences based on the matching design and explore sensitivity of the p. 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. Labor studies this paper considers causal inference and sample selection bias in nonexperimental settings in which. Estimatingcausal effectsfrom observationaldata withthe. 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.
Estimation of causal effects with multiple treatments. 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. 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. Heckman 1997 also played a role in the development of propensity score matching methods. 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. Pdf propensity score matching methods for nonexperimental. 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.
Research on matching methods for causal inference in. Comments on a critical appraisal of propensityscore matching in the medical literature between 1996 and 2003 by peter austin. 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. The essential role of balance tests in propensitymatched observational studies. Propensity score matching methods for nonexperimental causal studies. Propensity scores and causal inference learning methods. As compared to multivariate matching, propensity score matching eliminates the problem of finding an area of common support for all covariates. Nearest neighbor oneone or manyone matching, radius matching, kernel matching, mahalanobis metric matching, matching with a genetic algorithm. Propensity score estimation with boosted regression for evaluating causal effects in observational studies. The most common implementation of propensityscore matching is 1.
Reevaluating the evaluation of training programs rajeev h. Using the propensity score method to estimate causal effects. Why does propensity score matching work for causal inference. We discuss the use of propensity scorematching methods, and implement them using data from the national supported work experiment. This research aimed to estimate the effect of teaching management and planning on increasing academic planning behavior apb, using propensity score matching psm. Propensity score matching psm has become a popular approach to es. Propensity scorebased methods for causal inference in. We discuss the use of propensity score matching methods, and implement them using data from the national supported work experiment. Application of propensity score models in observational. Propensity score methods for causal inference with the.
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. As starr et a note, one then hew the dilemma that both. This article focuses on common methods for matching. Thus, you could infer that attending a catholic high school has an average causal treatment effect of 3. Section 2 presents the theory and motivation behind propensity scores, discussing how they are a crucial tool in the use of matching methods. Causal inference for statistics, social, and biomedical. In observational studies, the true propensity score is not, in general, known. Alternatives to matching methods include adjusting for background variables in a regression model, instrumental variables, structural equation modeling, or selection models. Propensity score matching in observational studies author. Abstract in a randomized study, subjects are randomly assigned to either a treated group or a control group.
The validity of inference will then strictly depend on the plausibility of the assumptions underlying the employed statistical techniques. Applyingthe ps thereare in generalthreedifferent methods. After propensity score adjustment, the distribution of baseline covariates will be balanced between treated and. 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. Propensity scorematching methods for nonexperimental causal studies rajeev h. 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. What are the best empirical studies comparing causal.
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. Cross validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. An application of kernelbased versus onetoone propensity score matching for a nonexperimental causal study. Propensityscore matching is used frequently in the medical literature 57. In other words, regardless of the exact technique used to adjust for the. Testing causal effects in observational survival data.
Probability of receiving the treatment given the observed covariates. Propensity score matching methods for nonexperimental causal studies rajeev h. The central role of the propensity score in observational. Application of propensity score models in observational studies nikki carroll, kaiser permanente colorado. Propensity score matching methods for nonexperimental causal. 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. Criticized by lalonde, defended by dehejia and wahba and critisized again by king and nielsen. This course will cover some of the most popular techniques for estimating causal effects with observational data. 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 fact, precisely because nonexperimental studies do not have the bene.
Using the propensity score the authors 2012 method to. This is all the more troubling because, although these theories require exact matching, most matching applications resort to some form of ex post strati. Also, there are few methods addressing the hidden bias in causal survival analysis. Propensity scorematching methods for nonexperimental causal. Propensity score matching can be conducted using a variety of statistical packages.
This paper considers causal inference and sample selection bias in nonexperimental settings in which. 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. View dehejia2002 from stat 6022 at university of cincinnati. Propensity score matching methods for nonexperimental causal studies article in ssrn electronic journal january 2002 with 102 reads how we measure reads. Users who downloaded this paper also downloaded these. Eric ej788243 using full matching to estimate causal. Propensity score methods have theoretical advantages over conventional covariate adjustment, but their relative performance in realword scenarios is poorly characterized. We propose the use of propensity score matching methods, and implement them using data from the nsw experiment.
However, when a study s sample size is not large, a. Matching methods such as nearest neighbor propensity score matching are increasingly popular techniques for controlling confounding in nonexperimental studies. Dehejia department of economics columbia university 420 w. Can do subsequent regression adjustment to eliminate residual imbalance in prognostically important covariates after. 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. A theory of statistical inference for matching methods in applied causal research stefano m. Scorea age schoolblackhispanic no degreemarriedre74re75 u74 u75 treatment effect means regression treatment effect nsw 185 0. Iacusy gary kingz giuseppe porrox november 22, 2015 abstract to reduce model dependence and bias in causal inference, researchers usually use. Presentation in the international methods colloquium. Estimation of causal effects using propensity score weighting. 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.
A key assumption in nonexperimental studies is that of strongly ignorable. Methods to conduct propensity score matching using sas are described in chapter 3 of faries, leon, maria haro, and obenchain 2010. The authors have written an important book, one that is accessible to students and provocatively argued. Combining groupbased trajectory modeling and propensity. An introduction to propensity score methods for reducing the. Causalcomparative studies attempt to establish causeeffect relationships. Propensity scores can also be used to weight observations like a sample. A general multivariate matching method for achieving balance in observational studies. Randomization, matching, and propensity scores in the design. 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. Propensity score matching developed as part of rubin causal model wikipedia contributors 2016. Propensity score matching methods for nonexperimental. Even in the absence of confounding by unmeaswd risk factors, the resulting data will be weak data usbss the ample size is very large or matching was done on most of the confounders. Application of propensity score models in observational studies.