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.

翻译：因果性在决策中的实际效用已被广泛认可，因果发现与因果推断本质上是相互交织的。然而，在因果发现方法的评估中，存在一个显著缺口，即对下游推断的重视不足。为解决这一缺口，我们在治疗效果估计的下游任务中，评估了六种已建立的基线因果发现方法以及一种基于GFlowNets的新方法。通过实施稳健的评估流程，我们针对这些因果发现方法在治疗效果估计中的有效性提供了宝贵见解，涵盖合成场景、真实世界场景以及低数据场景。此外，我们的研究结果表明，GFlowNets具备有效捕捉广泛、有用且多样化的ATE模式的能力。