ServiceNow Research

Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation


The practical utility of causality in decision-making is widely recognized, with causal discovery and inference being inherently intertwined. 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 six established baseline causal discovery methods and a newly proposed method based on GFlowNets, on the downstream task of treatment effect estimation. Through the implementation of a robust evaluation procedure, we offer valuable 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. Furthermore, the results of our study demonstrate that GFlowNets possess the capability to effectively capture a wide range of useful and diverse ATE modes.

Workshop on Structured Probabilistic Inference & Generative Modeling (ICML)
Alexandre Drouin
Alexandre Drouin
Research Lead

Research Lead at Human Decision Support located at Montreal, QC, Canada.

Yoshua Bengio
Yoshua Bengio
Research Advisor

Research Advisor at Human Decision Support located at Montreal, QC, Canada.