Bayesian nonparametric generative models for causal inference with missing at random covariates.
Biometrics
; 74(4): 1193-1202, 2018 12.
Article
en En
| MEDLINE
| ID: mdl-29579341
We propose a general Bayesian nonparametric (BNP) approach to causal inference in the point treatment setting. The joint distribution of the observed data (outcome, treatment, and confounders) is modeled using an enriched Dirichlet process. The combination of the observed data model and causal assumptions allows us to identify any type of causal effect-differences, ratios, or quantile effects, either marginally or for subpopulations of interest. The proposed BNP model is well-suited for causal inference problems, as it does not require parametric assumptions about the distribution of confounders and naturally leads to a computationally efficient Gibbs sampling algorithm. By flexibly modeling the joint distribution, we are also able to impute (via data augmentation) values for missing covariates within the algorithm under an assumption of ignorable missingness, obviating the need to create separate imputed data sets. This approach for imputing the missing covariates has the additional advantage of guaranteeing congeniality between the imputation model and the analysis model, and because we use a BNP approach, parametric models are avoided for imputation. The performance of the method is assessed using simulation studies. The method is applied to data from a cohort study of human immunodeficiency virus/hepatitis C virus co-infected patients.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Simulación por Computador
/
Causalidad
/
Teorema de Bayes
/
Biometría
Tipo de estudio:
Clinical_trials
/
Etiology_studies
/
Incidence_studies
/
Observational_studies
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Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Biometrics
Año:
2018
Tipo del documento:
Article
País de afiliación:
Estados Unidos
Pais de publicación:
Estados Unidos