habib61 / 2022-isba-bnpci

Github repo for the BNP Causal Inference short course

2022-ISBA-BNPCI

Github repo for the BNP Causal Inference short course.

• BART-Illustration.R illustrates the BART methodology in a non-causal setting, using the dbarts package.

• BCF-Meps.R implements the Bayesian Causal Forest of Hahn et al., illustrating on the Medical Expenditure Panel Survey (MEPS) to approximate the average causal effect of smoking on total medical expenditures.

• BCMF-Meps.R implements a Bayesian Causal Mediation Forest, illustrating on the MEPS to approximate the direct/indirect effect of smoking on total medical expenditures, as mediated by the overall health status of an individual.

• The repository https://github.com/YanxunXu/BaySemiCompeting contains code for implementing the semi-competing risks model described in the course.

• meps2011.csv is a subset of the MEPS dataset.

• ISBA_2022_slides.pdf contain the slides.

• This code requires the dbarts, bcf, SoftBart, and BartMediate packages to run. The packages dbarts and bcf are available on CRAN, while SoftBart and BartMediate are on GitHub at www.github.com/theodds/SoftBart and www.github.com/theodds/BartMediate respectively.

Bibliography

Forthcoming Book

• Daniels, M.J., Linero, A.R., and Roy, J.A. (2022+) Bayesian Nonparametrics for Causal Inference and Missing Data, CRC Press/Chapman & Hall.

BART

• Chipman, H.A., George, E.I., and McCulloch, R.E. (2010) BART: Bayesian Additive Regression Trees. Annals of Applied Statistics, 4(1) 266-298. Link.

• Hahn, P.R., Murray, J.S., and Carvalho, C.M. (2020). Bayesian regression tree models for causal inference: Regularization, confounding, and heterogeneous effects (with discussion). Bayesian Analysis 15(3), 965-1056. Link.

• Linero, A.R. and Zhang, Q. (2022+) Mediation Analysis Using Bayesian Tree Ensembles. To appear in Psychological Methods.

• Linero, A.R. and Yang, Y. (2018) Bayesian regression tree ensembles that adapt to smoothness and sparsity, Journal of the Royal Statistical Society, Series B, 80, 1087-1110. Link.

Misc

• Antonelli, J., Daniels, M.J. (2019). Discussion of 'PENCOMP'. Journal of the American Statistical Association, 40, 24-27. Link.

• Linero, A.R. (2022) Simulation‐based estimators of analytically intractable causal effects. To appear in Biometrics. Link.

Papers Using Dirichlet Processes

• Kim, C., Daniels, M.J., Marcus, B.H., and Roy, J.A. (2017). A framework for Bayesian nonparametric inference for causal effects of mediation. Biometrics, 73, 401-409. Link.

• Roy, J.A., Lum, K., and Daniels, M.J. (2017). A Bayesian nonparametric approach to marginal structural models for point treatments and a continuous or survival outcome. Biostatistics, 18, 32-47. Link.

• Roy, J.A., Lum, K., Daniels, M.J., Zeldow, B.Z., Dworkin, J., and Lo Re III, V. (2018). Bayesian nonparametric generative models for causal inference with missing at random covariates. Biometrics, 74, 1193-1202. Link.

• Xu, D., Daniels, M.J., and Winterstein, A.G. (2018). A Bayesian nonparametric approach to causal inference on quantiles, Biometrics, 74, 986-996. Link.

• Xu, Y., Scharfstein, D., Mueller, P., and Daniels, M.J. (2022) A Bayesian Nonparametric Approach for Evaluating the Causal Effect of Treatment in Randomized Trials with Semi-Competing Risks. Biostatistics, 23, 34-49. Link.

Missing Data

• Josefsson, M, Daniels, M.J. (2021). A Bayesian semi-parametric G-computation for causal inference in a cohort study with non-ignorable dropout and death. Journal of the Royal Statistical Society, Series C, 70, 398-414. Link.

• Linero, A. (2017) Bayesian nonparametric analysis of longitudinal studies in the presence of informative missingness, Biometrika, 104, 327-341. Link.

• Linero, A. and Daniels, M.J. (2015) A flexible Bayesian approach to monotone missing data in longitudinal studies with informative missingness with application to an acute schizophrenia clinical trial. Journal of the American Statistical Association, 110, 45-55. Link.

• Linero, A. and Daniels, M.J. (2018) A Bayesian approach for missing not at random outcome data: The role of identifying restrictions. Statistical Science, 33, 198-213. Link.

• Wang, C., Daniels, M. J., Scharfstein, D. O., and Land, S. (2010). A Bayesian shrinkage model for incomplete longitudinal binary data with application to the breast cancer prevention trial. Journal of the American Statistical Association, 105(492), 1333-1346. Link.