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Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation
Chris Emezue · Alexandre Drouin · Tristan Deleu · Stefan Bauer · Yoshua Bengio

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in Structured Probabilistic Inference and Generative Modeling »
Event URL: https://openreview.net/forum?id=9aDnWNPyeC »

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.

Keywords: Causal Inference · GFlowNets · causal discovery

Author Information

Chris Emezue (Technical University of Munich & Mila)
Alexandre Drouin (ServiceNow Research)
Tristan Deleu (Mila - Université de Montréal)
Stefan Bauer (KTH Stockholm)
Yoshua Bengio (Mila - Quebec AI Institute)

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