Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation
This work addresses a gap in evaluating causal discovery methods for practical decision-making in treatment effect estimation, though it is incremental as it benchmarks existing methods with a new addition.
The study evaluated seven Bayesian causal discovery methods, including a new GFlowNet-based approach, on their ability to improve downstream treatment effect estimation, finding that some algorithms effectively captured diverse ATE modes while others learned low-probability modes that reduced recall and precision.
The practical utility of causality in decision-making is widespread and brought about by the intertwining of causal discovery and causal inference. Nevertheless, a notable gap exists in the evaluation of causal discovery methods, where insufficient emphasis is placed on downstream inference. To address this gap, we evaluate seven established baseline causal discovery methods including a newly proposed method based on GFlowNets, on the downstream task of treatment effect estimation. Through the implementation of a distribution-level evaluation, we offer valuable and unique insights into the efficacy of these causal discovery methods for treatment effect estimation, considering both synthetic and real-world scenarios, as well as low-data scenarios. The results of our study demonstrate that some of the algorithms studied are able to effectively capture a wide range of useful and diverse ATE modes, while some tend to learn many low-probability modes which impacts the (unrelaxed) recall and precision.