ArticleObservational studies2025
What randomization can and cannot guarantee.
Article in Observational studies, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Abstract
Aronow et al. (2024) provide a great service to the causal inference community by delineating the key results in Robins and Ritov (1997). They show that randomized controlled trials (RCTs) ensure much stronger statistical inference than unconfounded observational studies even though nonparametric identification is identical in both settings. These results are in sharp contrast to the claim in Pearl and Mackenzie (2018) that RCTs are not the gold standard of causal analysis. Pearl and Mackenzie's (2018) claim is false and misleading for empirical researchers who want to infer causal effects based on data with finite sample sizes. I will further review what randomization can and cannot guarantee more broadly. In particular, I will highlight the value of randomization-based inference in RCTs, the limit of randomization alone for more complicated causal inference questions, and the importance of sensitivity analysis in observational studies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.