ArticleObservational studies2025
causalBETA: An R Package for Bayesian Semiparametric Causal Inference with Event-Time Outcomes.
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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Abstract
While randomized trials are the gold standard in causal inference, observational studies are often conducted when trials are infeasible either due to logistical or ethical constraints. Since treatments are not randomized in observational studies, techniques from causal inference are required to adjust for confounding. Bayesian approaches to causal estimation are desirable because they provide: 1) prior smoothing that offers useful regularization of causal effect estimates, 2) flexible models that are robust to misspecification, and 3) full inference (i.e., both point estimates and uncertainty quantification) for causal estimands. However, Bayesian causal inference is difficult to implement manually and there is a lack of user-friendly software, presenting a significant barrier to wide-spread use. Moreover, there is a lack of manuscripts aimed at explicitly connecting statistical/causal formula with implementation code. We address this gap by developing and describing causalBETA (
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Registered trials
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