The Subtype-Free Average Causal Effect for Heterogeneous Disease Etiology
Amit Sasson, Molin Wang, Shuji Ogino, Daniel Nevo

TL;DR
This paper introduces the Subtype-Free Average Causal Effect (SF-ACE), a new causal estimand for understanding how exposures like smoking affect specific disease subtypes, accounting for heterogeneity and untestable assumptions.
Contribution
It proposes the SF-ACE using principal stratification, explores its identification under various assumptions, and develops sensitivity analysis and estimators, including a doubly-robust method.
Findings
Applied to cohort data, revealing heterogeneity in smoking's effect on colorectal cancer subtypes.
Developed multiple estimators, including a doubly-robust one, for the SF-ACE.
Provided sensitivity analysis techniques to assess assumptions' impact.
Abstract
Studies have shown that the effect an exposure may have on a disease can vary for different subtypes of the same disease. However, existing approaches to estimate and compare these effects largely overlook causality. In this paper, we study the effect smoking may have on having colorectal cancer subtypes defined by a trait known as microsatellite instability (MSI). We use principal stratification to propose an alternative causal estimand, the Subtype-Free Average Causal Effect (SF-ACE). The SF-ACE is the causal effect of the exposure among those who would be free from other disease subtypes under any exposure level. We study non-parametric identification of the SF-ACE, and discuss different monotonicity assumptions, which are more nuanced than in the standard setting. As is often the case with principal stratum effects, the assumptions underlying the identification of the SF-ACE from…
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Taxonomy
TopicsAdvanced Causal Inference Techniques · Statistical Methods and Inference
