Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation
面向后续处理效应估计的贝叶斯因果发现方法评测
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…