Bayesian causal inference for observational studies with missingness in covariates and outcomes.

ABSTRACT: Missing data are a pervasive issue in observational studies using electronic health records or patient registries. It presents unique challenges for statistical inference, especially causal inference. Inappropriately handling missing data in causal inference could potentially bias causal estimation. Besides missing data problems, observational health data structures typically have mixed-type variables - continuous and categorical covariates - whose joint distribution is often too complex to be modeled by simple parametric models. The existence of missing values in covariates and outcomes makes the causal inference even more challenging, while most standard causal inference approaches assume fully observed data or start their works after imputing missing values in a separate preprocessing s

SUBMITTER: Zang H 

PROVIDER: S-EPMC10840608 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature