The Blessings of Multiple Treatments and Outcomes in Treatment Effect Estimation

TL;DR

This paper introduces a new causal inference setting involving multiple treatments and outcomes, proposing methods to leverage their interrelations for better confounding adjustment and causal effect estimation.

Contribution

It extends existing methods to handle multiple treatments and outcomes simultaneously, enabling causal identification through proxy variables in complex scenarios.

Findings

Effective causal discovery method demonstrated on synthetic data.

Improved estimation accuracy in sepsis disease case study.

Proves that multiple outcomes can mutually aid in causal identification.

Abstract

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.