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关系是一个具有挑战性的问题。现有研究利用代理变量或多重治疗来调整混杂偏倚。特别是后者方法将单一结局的影响归因于多种治疗，从而能够估计潜在变量以控制混杂。然而，这些方法主要关注单一结局，而在许多现实场景中，研究者更关注对多重结局的影响。此外，这些结局往往与多重治疗相互关联。例如在重症监护病房（ICU）中，医护人员需要评估多种疗法对多项健康指标的有效性。为适应这类场景，我们提出一种名为"多重治疗与多重结局"的新设置。我们证明，在该设置下对多重结局的平行研究能够相互辅助进行因果识别，即可以利用其他治疗和结局作为所研究治疗效应的代理变量。我们进一步提出一种因果发现方法，可有效识别此类用于因果估计的代理变量。通过合成数据与脓毒症病例验证了该方法的实用性。