ArticleBiometrics2018
Bayesian nonparametric generative models for causal inference with missing at random covariates.
Article in Biometrics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Bayesian Causal Methods for Environmental Accountability Studies with Heterogeneous Effects.Current environmental health reports · 2026Review
- Assessing Unmeasured Confounding in Observational Research via Bayesian Semiparametric Modeling.Observational studies · 2026Article
- Enriched Pitman-Yor processes.Scandinavian journal of statistics, theory and applications · 2025Article
- Bayesian Nonparametric Model for Heterogeneous Treatment Effects With Zero-Inflated Data.Statistics in medicine · 2024Article
- A Bayesian nonparametric approach for multiple mediators with applications in mental health studies.Biostatistics (Oxford, England) · 2024Article
- A Bayesian nonparametric approach for causal mediation with a post-treatment confounder.Biometrics · 2024Article
- Confounder-dependent Bayesian mixture model: Characterizing heterogeneity of causal effects in air pollution epidemiology.Biometrics · 2024Article
- Dirichlet process mixture models to impute missing predictor data in counterfactual prediction models: an application to predict optimal type 2 diabetes therapy.BMC medical informatics and decision making · 2024Article
- Bayesian causal inference for observational studies with missingness in covariates and outcomes.Biometrics · 2023Article
- Article
- Bayesian clustering of multiple zero-inflated outcomes.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Article
- Bayesian data fusion: Probabilistic sensitivity analysis for unmeasured confounding using informative priors based on secondary data.Biometrics · 2022Article
- Measured and accounted-for confounding in pharmacoepidemiologic studies: Some thoughts for practitioners.Pharmacoepidemiology and drug safety · 2021Review
- Bayesian semi-parametric G-computation for causal inference in a cohort study with MNAR dropout and death.Journal of the Royal Statistical Society. Series C, Applied statistics · 2021Article
- A practical introduction to Bayesian estimation of causal effects: Parametric and nonparametric approaches.Statistics in medicine · 2021Article
- Discussion of PENCOMP.Journal of the American Statistical Association · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.