Propensity Score Methods for Causal Inference with the PSMATCH Procedure - Dr. G. Gordon Brown, SAS
In a randomized study, subjects are randomly assigned to either a treated group or a control group. Random assignment ensures that the distribution of the covariates is the same in both groups and that the treatment effect can be estimated by directly comparing the outcomes for the subjects in the two groups. In contrast, subjects in an observational study are not randomly assigned. In order to establish causal interpretations of the treatment effects in observational studies, special statistical approaches that adjust for the covariate confounding are required to obtain unbiased estimation of causal treatment effects.
One strategy for correctly estimating the treatment effect is based on the propensity score, which is the conditional probability of the treatment assignment given the observed covariates. Prior to the analysis, you use propensity scores to adjust the data by weighting observations, stratifying subjects that have similar propensity scores, or matching treated subjects to control subjects.
This paper reviews propensity score methods for causal inference and introduces the PSMATCH procedure, which is new in SAS/STAT® 14.2. The procedure provides methods of weighting, stratification, and matching. Matching methods include greedy matching, matching with replacement, and optimal matching. The procedure assesses covariate balance by comparing distributions between the adjusted treated and control groups.
Click here to download the companion paper.
One strategy for correctly estimating the treatment effect is based on the propensity score, which is the conditional probability of the treatment assignment given the observed covariates. Prior to the analysis, you use propensity scores to adjust the data by weighting observations, stratifying subjects that have similar propensity scores, or matching treated subjects to control subjects.
This paper reviews propensity score methods for causal inference and introduces the PSMATCH procedure, which is new in SAS/STAT® 14.2. The procedure provides methods of weighting, stratification, and matching. Matching methods include greedy matching, matching with replacement, and optimal matching. The procedure assesses covariate balance by comparing distributions between the adjusted treated and control groups.
Click here to download the companion paper.
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About the Presenter
Dr G. Gordon Brown is a Senior Research Statistician in the Statistical Applications R&D department. Gordon performed contract research for 14 years before joining SAS in 2015. He has a PhD in statistics from North Carolina State University and has been a SAS user since 1989.
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