As a substitute, randomized controlled trial (RCT) researchers increasingly rely on propensity score modeling (PSM) to estimate causal effects. However, some warn about the dangers of placing too much blind faith in the abilities of PSM. This study tests the reliability and validity of seven common PSM methods in their ability to remove an artificial selection bias and replicate results from several RCTs in criminal justice data. Findings suggest PSM can be an effective means for simulating RCT results. Meta-analyses reveal the average difference between PSM and RCT estimates were relatively small. Ultimately, our findings suggest that PSM can be an effective means for simulating an RCT while also harboring reason for concern. Researchers and policy-makers should approach the use and interpretation of PSM with cautious optimism as it appears to provide a reliable and valid estimate of the treatment effect most of the time.
