Local causal discovery in epidemiology: an application to quantifying the effect of diabetes on severe liver fibrosis in patients with viral hepatitis
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Background
Estimating the controlled direct effect (CDE) from observational data is challenging when the DAG is unknown. Causal discovery methods can infer a partially oriented DAG, enabling the identification of potential adjustment sets. We use a local causal discovery algorithm that focuses on the relevant portion of the graph, reducing assumptions and complexity compared to global methods. This approach is applied to a viral hepatitis cohort to estimate the CDE of diabetes on severe liver fibrosis.
Methods
The CDE of diabetes on liver fibrosis in patients with HBV or HCV was assessed using baseline data from the French ANRS CO22 HEPATHER cohort initiated in 2012. A local causal discovery algorithm, LocalPC-CDE, with bootstrap augmentation identified a robust adjustment set, retaining only variables minimally affected by sampling variability. The CDE was quantified as a causal odds ratio using logistic regression.
Results
Causal discovery included 20858 patients, with estimation performed on 8802 completecase observations. The algorithm identified an adjustment set of seven variables: geographical origin, age, hepatitis type, total cholesterol, HDL cholesterol, past alcohol consumption, blood glucose, and sex. The CDE of diabetes on severe fibrosis in viral hepatitis patients was significantly positive, with an estimated odds ratio of 2.03 (95% CI [1.78, 2.31]).
Conclusions
After causal adjustment using a targeted, data-driven approach, diabetes retained a direct and statistically significant effect on liver fibrosis in patients with chronic viral hepatitis. This paper more generally introduces a methodological pipeline for local causal discovery when the underlying DAG is uncertain.
