Primary Submission Category: Bayesian Causal Inference
A Bayesian Semiparametric Approach to Treatment Effect Variation with Noncompliance
Authors: Jared Fisher, David Puelz, Sameer Deshpande,
Presenting Author: Jared Fisher*
Estimating varying treatment effects in randomized trials with noncompliance is inherently challenging since variation comes from two separate sources: variation in the impact itself and variation in the compliance rate. In this setting, existing Frequentist and ML-based methods are quite flexible but are highly sensitive to the so-called weak instruments problem, in which the compliance rate is (locally) close to zero, and require pre-specifying subgroups of interest. Parametric Bayesian approaches, which account for noncompliance via imputation, are more robust in this case, but are much more sensitive to model specification. In this paper, we propose a Bayesian semiparametric approach that combines the best features of both approaches. Our main methodological contribution is to present a Bayesian Causal Forest model for binary response variables in scenarios with noncompliance. In this Bayesian noncompliance framework, we repeatedly impute individuals’ compliance types, allowing us to flexibly estimate varying treatment effects among compliers while mitigating the weak instruments problem. We then apply the method to the detect and analyze heterogeneity in study of workplace wellness, where there are a plethora of binary outcomes of interest.
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