Assessing Interactive Causes of an Occurred Outcome Due to Two Binary Exposures
Shanshan Luo, Wei Li, Xueli Wang, Shaojie Wei, Zhi Geng

TL;DR
This paper develops a method to assess the interactive causal effects of two binary exposures on a binary outcome, using posterior probabilities and secondary outcomes, with application to smoking and asbestos exposure in lung cancer.
Contribution
It introduces a novel approach to identify and quantify the interactive causes of an observed outcome using posterior probabilities and secondary outcomes.
Findings
The method successfully identifies the primary cause as the interaction between smoking and asbestos.
Application to lung cancer shows the disease is mainly due to the synergistic effect.
The approach enhances causal attribution analysis for binary exposures and outcomes.
Abstract
In contrast to evaluating treatment effects, causal attribution analysis focuses on identifying the key factors responsible for an observed outcome. For two binary exposure variables and a binary outcome variable, researchers need to assess not only the likelihood that an observed outcome was caused by a particular exposure, but also the likelihood that it resulted from the interaction between the two exposures. For example, in the case of a male worker who smoked, was exposed to asbestos, and developed lung cancer, researchers aim to explore whether the cancer resulted from smoking, asbestos exposure, or their interaction. Even in randomized controlled trials, widely regarded as the gold standard for causal inference, identifying and evaluating retrospective causal interactions between two exposures remains challenging. In this paper, we define posterior probabilities to characterize…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Causal Inference Techniques · Bayesian Modeling and Causal Inference · Qualitative Comparative Analysis Research
