Six‐way decomposition of causal effects: Unifying mediation and mechanistic interaction

doi:10.1002/sim.8708

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Title:	Six‐way decomposition of causal effects: Unifying mediation and mechanistic interaction
Authors:	Huang, Yen‐Tsung;Tai, An‐Shun;Chou, Meng‐Ying;Lin, Geng‐Xian;Lin, Sheng‐Hsuan
Keywords:	causal inference;effect decomposition;interaction;mediation;sufficient component cause
Date:	2020-11-30
Issue Date:	2021-03-24 12:12:43 (UTC+8)
Abstract:	The sufficient component cause (SCC) model and counterfactual model are two common methods for causal inference, each with their own advantages: the SCC model allows the mechanistic interaction to be detailed, whereas the counterfactual model features a systemic framework for quantifying causal effects. Hence, integrating the SCC and counterfactual models may facilitate the conceptualization of causation. Based on the marginal SCC (mSCC) model, we propose a novel counterfactual mSCC framework that includes the steps of definition, identification, and estimation. We further propose a six‐way effect decomposition for assessing mediation and the mechanistic interaction. The results demonstrate that when all variables are binary, the six‐way decomposition is an extension of four‐way decomposition and that without agonism, the six‐way decomposition is reduced to four‐way decomposition. To illustrate the utility of the proposed decomposition, we apply it to a Taiwanese cohort to examine the mechanism of hepatitis C virus (HCV)‐induced hepatocellular carcinoma (HCC) with liver inflammation measured by alanine aminotransferase (ALT) as a mediator. Among the HCV‐induced HCC cases, 62.27% are not explained by either mediation or interaction in relation to ALT; 9.32% are purely mediated by ALT; 16.53% are caused by the synergistic effect of HCV and ALT; and 9.31% are due to the mediated synergistic effect of HCV and ALT. In summary, we introduce an SCC model framework based on counterfactual theory and detail the required identification assumptions and estimation procedures; we also propose a six‐way effect decomposition to unify mediation and mechanistic interaction analyses.
Relation:	Statistics in Medicine 39(27), p.4051-4068
DOI:	10.1002/sim.8708
Appears in Collections:	[Graduate Institute & Department of Mathematics] Journal Article

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