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Observational studies are often conducted to estimate causal effects oftreatments or exposures on event-time outcomes. Since treatments are notrandomized in observational studies, techniques from causal inference arerequired to adjust for confounding. Bayesian approaches to causal estimates aredesirable because they provide 1) prior smoothing provides usefulregularization of causal effect estimates, 2) flexible models that are robustto misspecification, 3) full inference (i.e. both point and uncertaintyestimates) for causal estimands. However, Bayesian causal inference isdifficult to implement manually and there is a lack of user-friendly software,presenting a significant barrier to wide-spread use. We address this gap bydeveloping causalBETA (Bayesian Event Time Analysis) - an open-source R packagefor estimating causal effects on event-time outcomes using Bayesiansemiparametric models. The package provides a familiar front-end to users, withsyntax identical to existing survival analysis R packages such as survival. Atthe same time, it back-ends to Stan - a popular platform for Bayesian modelingand high performance statistical computing - for efficient posteriorcomputation. To improve user experience, the package is built using customizedS3 class objects and methods to facilitate visualizations and summaries ofresults using familiar generic functions like plot() and summary(). In thispaper, we provide the methodological details of the package, a demonstrationusing publicly-available data, and computational guidance.

causalBETA: An R Package for Bayesian Semiparametric Causal Inference with Event-Time Outcomes