Apportioning Causal Responsibility of Two Risk Factors for an Adverse Outcome via Counterfactual Attribution

2026-06-16

AI Summary

This study addresses the problem of quantitatively attributing causal responsibility to two binary risk factors following an adverse outcome. The authors propose a framework for average causal responsibility based on the distribution of latent causal types. Under assumptions of no confounding and monotonicity, they achieve nonparametric identification of this responsibility through structural balancing conditions—a result established here for the first time. When these assumptions fail to hold, the method yields sharp bounds instead. Integrating potential outcomes, causal type analysis, and counterfactual reasoning, the approach is successfully applied to the canonical case of lung cancer jointly caused by smoking and asbestos exposure, enabling a quantitative decomposition of their respective causal contributions.

Abstract

Unlike traditional causal inference, which prospectively evaluates the effects of causes, apportioning causal responsibility requires a retrospective assessment to deduce the causes of an outcome that has already occurred. This paper proposes a quantitative framework for apportioning causal responsibility between two binary risk factors that jointly contribute to a realized adverse outcome. Ideally, knowing the individual's latent causal type, defined by the potential outcomes under all possible exposure combinations, would allow precise apportionment; however, these potential outcomes cannot be simultaneously observed. We therefore define the average causal responsibility of each risk factor as its expected responsibility over the distribution of latent causal types. Under the assumptions of no confounding and monotonicity, we establish nonparametric identification of this metric when the type-specific responsibilities satisfy a structural balance condition, and derive sharp bounds otherwise. We illustrate the proposed framework using the classic example of lung cancer attributable to smoking and asbestos exposures.

Problem

• causal responsibility
• counterfactual attribution
• risk factors
• adverse outcome
• latent causal type

Innovation

• causal responsibility
• counterfactual attribution
• latent causal types
• nonparametric identification
• structural balance