2020/06/24 by Ethan Steinberg, Steinberg, Ethan, Steve Yadlowsky +7
Economics, Econometrics and Finance · Mathematics · #Advanced Causal Inference Techniques #Applications (stat.AP) #Computer science #FOS: Computer and information sciences #Health Systems, Economic Evaluations, Quality of Life #Healthcare Policy and Management #Mathematics #Observational methods in psychology #Observational study #Propensity score matching #Statistics #stat.AP
paper · pdf · doi:10.48550/arxiv.2006.14102
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2020/06/24 · arxiv created 2022/09/13 · arxiv updated 2022/09/14 · openalex created_date 2023/02/14 · openalex updated_date 2026/08/05
Observational studies are valuable for estimating the effects of various medical interventions, but are notoriously difficult to evaluate because the methods used in observational studies require many untestable assumptions. This lack of verifiability makes it difficult both to compare different observational study methods and to trust the results of any particular observational study. In this work, we propose TrialVerify, a new approach for evaluating observational study methods based on ground truth sourced from clinical trial reports. We process trial reports into a denoised collection of known causal relationships that can then be used to estimate the precision and recall of various observational study methods. We then use TrialVerify to evaluate multiple observational study methods in terms of their ability to identify the known causal relationships from a large national insurance claims dataset. We found that inverse propensity score weighting is an effective approach for accurately reproducing known causal relationships and outperforms other observational study methods. TrialVerify is made freely available for others to evaluate observational study methods.