Andrews B, Wongchokprasitti C, Visweswaran S, Lakhani CM, Patel CJ, Cooper GF. A new method for estimating the probability of causal relationships from observational data: Application to the study of the short-term effects of air pollution on cardiovascular and respiratory disease.
Artif Intell Med 2023;
139:102546. [PMID:
37100513 PMCID:
PMC10171833 DOI:
10.1016/j.artmed.2023.102546]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 04/04/2023] [Accepted: 04/04/2023] [Indexed: 04/28/2023]
Abstract
In this paper we investigate which airborne pollutants have a short-term causal effect on cardiovascular and respiratory disease using the Ancestral Probabilities (AP) procedure, a novel Bayesian approach for deriving the probabilities of causal relationships from observational data. The results are largely consistent with EPA assessments of causality, however, in a few cases AP suggests that some pollutants thought to cause cardiovascular or respiratory disease are associated due purely to confounding. The AP procedure utilizes maximal ancestral graph (MAG) models to represent and assign probabilities to causal relationships while accounting for latent confounding. The algorithm does so locally by marginalizing over models with and without causal features of interest. Before applying AP to real data, we evaluate it in a simulation study and investigate the benefits of providing background knowledge. Overall, the results suggest that AP is an effective tool for causal discovery.
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