Assessing causal effects in the presence of unobserved confounding is a challenging problem. Existing studies leveraged proxy variables or multiple treatments to adjust for the confounding bias. In particular, the latter approach attributes the impact on a single outcome to multiple treatments, allowing estimating latent variables for confounding control. Nevertheless, these methods primarily focus on a single outcome, whereas in many real-world scenarios, there is greater interest in studying the effects on multiple outcomes. Besides, these outcomes are often coupled with multiple treatments. Examples include the intensive care unit (ICU), where health providers evaluate the effectiveness of therapies on multiple health indicators. To accommodate these scenarios, we consider a new setting dubbed as multiple treatments and multiple outcomes. We then show that parallel studies of multiple outcomes involved in this setting can assist each other in causal identification, in the sense that we can exploit other treatments and outcomes as proxies for each treatment effect under study. We proceed with a causal discovery method that can effectively identify such proxies for causal estimation. The utility of our method is demonstrated in synthetic data and sepsis disease.

翻译：在存在未观测混杂的情况下评估因果效应是一项具有挑战性的问题。现有研究利用代理变量或多重处理来调整混杂偏差。特别是，后一种方法将影响归因于单一结果的多重处理，从而允许估计潜在变量以控制混杂。然而，这些方法主要关注单一结果，而在许多现实场景中，研究对多重结果的影响更为重要。此外，这些结果通常与多重处理相关联。例如，在重症监护病房（ICU）中，医疗人员评估多种疗法对多项健康指标的效果。为适应这些场景，我们考虑了一种称为“多重处理与多重结果”的新设置。随后，我们证明该设置中涉及的多重结果的平行研究可以在因果识别中相互促进，即我们可以利用其他处理与结果作为所研究每种处理效应的代理变量。我们进而提出一种因果发现方法，能够有效识别此类代理变量以进行因果估计。我们的方法在合成数据与脓毒症数据中的实用性得到了验证。