Research map / Probabilistic and scientific modeling / Causal discovery
Causal Mixture Models: Characterization and Discovery
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Introduces causal mixture models to represent datasets that combine unobserved subpopulations with distinct causal generating processes, and studies their characterization and discovery.
Problem
Observational data often mixes unobserved subpopulations that follow different causal mechanisms; ignoring this heterogeneity can bias causal effect estimates (e.g., drug effectiveness with drug-resistant subgroups).
Approach
Formulates causal mixture models as a way to explicitly model a dataset as a mixture of latent subpopulations, each with its own causal generating process, and addresses characterization and discovery of such models. Only the abstract was available, so finer details are not stated.
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