Propensity score matching allows one to estimate the att imbens, 2004. The propensity score plays a central role in a variety of causal inference settings. Propensity score matching psm aims to equate treatment groups with respect to. Is taking math lessons after school helpful in improving score. 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 king. Propensity score matching regression discontinuity limited. The use of propensity score matching in the evaluation of active labour market policies a study carried out on behalf of the department for work and pensions by alex bryson, richard dorsett and susan purdon policy studies institute and national centre for social research. A propensity score isnt just a way of matching groups. Matching now we will match patients in each treatment on propensity score using the psmatch2 command in stata. The potential bias from ffm increases as treatment groups become more dissimilar. 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. Propensity score matching in university of manitoba. Favored not for the overall estimate as much as the substantive value.
In the statistical analysis of observational data, propensity score matching psm is a statistical matching technique that attempts to estimate the effect of a. The use of propensity score methods in psychotherapy research. Propensity score matching in accounting research aaa digital. 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.
Multivariate and propensity score matching software with. 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. The propensity score ps was transformed to logitps as it has been shown that a greater match can be obtained by using this transformation of the ps and matching within ps calipers is more accurate than matching on just the raw ps 3. In this paper, we focus on propensity score matching and consider di erent approaches to take into account the clustered structure of the data with the aim of reducing the bias due to clusterlevel confounders. Compared to the older style propensity matching to create a pseudo control sample, it may be better to weight the full data by inverse propensity score because it doesnt discard data. Labor studies program propensity score matching estimators rosenbaum and rubin, 1983 are widely used in evaluation research to estimate average treatment effects. Covariate balancing propensity score kosuke imai and marc ratkovic princeton university, usa received april 2012. Ensure that covariates are balanced across treatment and comparison groups in sample matched or weighted by propensity score step 6. Propensity score can be used in several different ways, including restriction, stratification, matching, modeling, or weighting to account for confounding effects. In the last column we can see the smd, here we should be careful about smds which are greater than 0. Nov 01, 2010 a conceptual framework for propensity score matching. How are propensity scores different from adding covariates in a regression, and when are they preferred to the latter. Why propensity scores should not be used for matching.
Its important to match on any potential confounders as well as any imbalanced covariates. Estimating and using propensity score in presence of missing. An introduction to implementing propensity score matchingwith sas kathy hardis fraeman, united biosource corporation, bethesda, md. Alternatives to matching stratification on the propensity score. The performance of different propensity score methods for. An introduction to implementing propensity score matching. Chapter iii focuses on applications of marginal structural. We describe the use of propensity score matching psm to select control local authority areas lau to evaluate the public health impact of alcohol policies for 1 prospective evaluation of alcohol policies using arealevel data, and 2 a novel twostage quasi casecontrol design. My book practical propensity score methods using r was a product of my teaching and research, and it aims to facilitate the work of researchers and graduate students interested in estimating treatment effects with observational data. Propensity score matching psm has become a popular approach to estimate causal treatment effects. Some parts of the notes are taken from various sources listed below and no originality is claimed. 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. Combining propensity score matching with additional adjustments for prognostic covariates donald b. Practical propensity score methods using r practical.
When this is adjusted for in any one of a number of ways including matching you theoretically break one of the conditions necessary for confounding. Five subclasses are expected to remove 90% of bias from modeled covariates. Estimating the benefit incidence of an antipoverty program by. It is widely applied when evaluating labour market policies. 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. Propensity score matching matching and propensity scores. Matching on the estimated propensity score mit economics. There are several different options of how ps can then be used to control for confounding. Propensity score matching psm refers to the pairing of treatment and control units with similar values on. Outline 1 observational studies and propensity score 2 motivating example.
Using propensity score methods to analyse individual patient. In particular, matching and weighting methods based on the estimated propensity score have. In the potential outcomes framework proposed by rubin, each subject has a pair of potential outcomes. Combining propensity score matching with additional. There are other ways to use propensity scores at its heart, its a way to characterize the probability of being exposed given covariates. 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, inefficiency, model dependence, and bias. Chapter forthcoming in the the oxford handbook of quantitative methods. Arealevel public health interventions can be difficult to evaluate using natural experiments. Propensity score matching estimators rosenbaum and rubin, 1983 are widely used in evaluation research to estimate average treatment effects. 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. Using propensity score methods to analyse individual.
I have been teaching and doing research about propensity score methods at university of florida for over 10 years. Bias reduction in observational cohort studies obtaining matched cohorts using propensity scores shelley fordred, savvy stats ltd, heston, uk victor kiri, fv and jk consulting, surrey, uk. Rubin, 1983 is a widely used tool for determining causal effects from observational data. Propensity score matching psm refers to the pairing of. A comparison of outcomes is made between treated and control groups. Propensity score matching and related methods a number of different propensity score matching methods can be used to match units on their propensity scores. This video provides an introduction to propensity score matching, and explains why it is a useful concept for deriving estimates for the average causal effect. Propensity score matching in r matching and propensity. The first author was supported in part by a grant from the w. Karr abstract propensity score matching is often used in observational studies to create treatment and control groups with similar distributions of observed covariates.
A methodmacro based on propensity score and mahalanobis distance to reduce bias in treatment comparison in observational study. Propensity score matching for causal inference with relational data. This function finds optimal balance using multivariate matching where a genetic search algorithm determines the weight each covariate is given. Here, the counterfactual group is constructed by matching program participants to nonparticipants from a larger survey such as the population census or an annual national budget survey. Performing a regression rather than simple cross tabs after the weighting or matching is a good idea to handle inevitable imperfections.
Propensity score matching in observational studies author. Basics of matching general strategy of matching 1 determine the variables you want to match on. We will specify the common support option so that it. Rubin and neal thomas propensity score matching refers to a class of multivariate methods used in comparative studies to construct treated and matched control samples that have similar distributions on many covariates. Propensity score matching iza institute of labor economics. One reason i was leaning toward using a propensity score model is because the results will likely be shownread by a nontechnical audience of higher education administrators. What, when, and how article pdf available in the journal of early adolescence 341. These include regression adjustment in form of including the ps as a covariate or weight into the final outcome model, as well as stratification, or matching based on the ps. This is in line with results in rubin and thomas 1992ab who argue that, in settings with normally distributed covariates, matching on the estimated rather than the. Propensity score matching for selection of local areas as. A propensity score is the probability that an individual will be assigned to a condition or group, given a set of covariates when the assignment is made. So previously, we noted that a propensity score is a balancing score, so matching on a propensity score should achieve balance. Interactions in propensity score models cross validated.
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. An introduction to propensity score matching ut liberal arts. Lecture notes on propensity score matching jinlung lin this lecture note is intended solely for teaching. We found that both propensity score matching and iptw using the propensity score allow for the estimation of marginal hazard ratios with minimal bias. Probability of assignment to a 10% relative to a 5% rate increase. Proceed with analyses based on sample matched or weighted by propensity score calculating a propensity score is an iterative process. We will specify the common support option so that it will leave out cases that lie outside the range of. Propensity score matching then uses ex, or a monotone function of it, to select control subjects, for example, choosing in turn for each of the nt treated subjects the closest not yet chosen control subject nearest remaining neighboror nearest available matching. Bin the sample into quintiles or finer by propensity score. Some practical guidance for the implementation of propensity. Some of the concepts that we will cover include calipers, propensity score overlap and trimming the tails of a propensity score distribution. The purpose of this paper is to reduce barriers to the use of this statistical method by presenting the theoretical framework and an illustrative example of propensity.
This one involves several steps including how to fit a propensity score model in r, how to actually match on the propensity score and then how to analyze the data after matching. Steiner northwestern university david cook abt associates inc. Multivariate and propensity score matching estimator. A practical guide to getting started with propensity scores. An illustrative example of propensity score matching with. 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. Psm attempts to reduce the bias due to confounding variables that could be found in an estimate of the treatment effect. Thus, matching on the estimated propensity score has a smaller asymptotic variance than matching on the true propensity score. Multivariate and propensity score matching software with automated balance optimization.
To learn how to create propensity scores apply them in a variety of analytic approaches in stata. Grant foundation and grants r305u070003 and r305d33 from the institute for educational sciences. Feb 06, 2014 this video provides an introduction to propensity score matching, and explains why it is a useful concept for deriving estimates for the average causal effect. Y i 0 and y i 1, the outcomes under the control and active treatments, respectively. To explain further, if propensity score matching was used in a randomized experiment. We addressed this issue by using a propensityscore matching procedure that enabled us to match early and late. Matching approach the propensity score ps was transformed to logitps as it has been shown that a greater match can be obtained by using. An introduction to propensity score methods for reducing. Propensity scores for the estimation of average treatment. Propensity score matching for bias reduction in the comparison of a treatment to a nonrandomized. Estimating and using propensity score in presence of missing background data an application to assess the impact of childbearing on wellbeing alessandra mattei and fabrizia mealli abstract estimation of average treatment e. 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. Treatment evaluation is the estimation of the average effect of a program or treatment on the outcome of interest. 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.
Most administrators i talk to seem to be more familiar with cohort matching either onetoone, or oneto many designs. Among such methods, we discussed the propensity score matching method that is commonly used in medical research studies. Typically, propensity scores are estimated using logistic. We show that propensity score matching psm, an enormously popular method of preprocessing data for causal inference, often accomplishes.
In this video, we will discuss how to carry out a matched propensity score analysis in r. The whole family of methods doesnt necessarily deliver big gains over. Five steps to successfully implement and evaluate propensity. Introduction to propensity scores in stata learning objectives. It is widely applied when evaluating labour market policies, but empirical examples can be found in very diverse fields of study. 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.
To understand the use of propensity scores as a means for controlling for selection bias in observational studies of treatment effects. Estimation of propensity scores using generalized additive models mija woo. Matching on the estimated propensity score alberto abadie, guido w. Propensity score matching estimators rosenbaum and rubin 1983 are widely used in evaluation research to estimate average treatment effects. How are propensity scores different from adding covariates. Bias reduction in observational cohort studies obtaining. 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. The problem is that the method remains underutilized despite increased calls in the literature for its use. Estimation of propensity scores using generalized additive. Propensity score matching psm techniques alleviate these concerns by. Four different propensity score methods are used for removing the effects of confounding when estimating the effects of treatment on outcomes. Balance is determined by examining cumulative probability distribution functions of a variety of standardized statistics.
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