The DOI matches its Crossref record; 62 of the 62 references it cites were found in a registry or an index.
Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects (with Discussion)
Open accessAbstract
This paper presents a novel nonlinear regression model for estimating heterogeneous treatment effects, geared specifically towards situations with small effect sizes, heterogeneous effects, and strong confounding by observables. Standard nonlinear regression models, which may work quite well for prediction, have two notable weaknesses when used to estimate heterogeneous treatment effects. First, they can yield badly biased estimates of treatment effects when fit to data with strong confounding. The Bayesian causal forest model presented in this paper avoids this problem by directly incorporating an estimate of the propensity function in the specification of the response model, implicitly inducing a covariate-dependent prior on the regression function. Second, standard approaches to response surface modeling do not provide adequate control over the strength of regularization over effect heterogeneity. The Bayesian causal forest model permits treatment effect heterogeneity to be regularized separately from the prognostic effect of control variables, making it possible to informatively “shrink to homogeneity”. While we focus on observational data, our methods are equally useful for inferring heterogeneous treatment effects from randomized controlled experiments where careful regularization is somewhat less complicated but no less important. We illustrate these benefits via the reanalysis of an observational study assessing the causal effects of smoking on medical expenditures as well as extensive simulation studies.
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Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
The central role of the propensity score in observational studies for causal effects
ROSENBAUM, P. R., & RUBIN, D. B. (1983) · Biometrika · doi:10.1093/biomet/70.1.41 · Cited by 31,241
Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
Imbens, G. W., & Rubin, D. B. (2015) · doi:10.1017/cbo9781139025751 · Cited by 4,210
Gelman, A. (2006) · Bayesian Analysis · doi:10.1214/06-ba117a · Cited by 4,050
Double/debiased machine learning for treatment and structural parameters
Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018) · The Econometrics Journal · doi:10.1111/ectj.12097 · Cited by 2,946
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
Wager, S., & Athey, S. (2018) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1319839 · Cited by 2,917
Doubly robust estimation in missing data and causal inference models
Bang, H., & Robins, J. M. (2005) · Biometrics · doi:10.1111/j.1541-0420.2005.00377.x · Cited by 1,998
Athey, S., Tibshirani, J., & Wager, S. (2019) · The Annals of Statistics · doi:10.1214/18-aos1709 · Cited by 1,870
BART: Bayesian additive regression trees
Chipman, H. A., George, E. I., & McCulloch, R. E. (2010) · The Annals of Applied Statistics · doi:10.1214/09-aoas285 · Cited by 1,542
A national experiment reveals where a growth mindset improves achievement
Yeager, D. S., Hanselman, P., Walton, G. M., Murray, J. S., Crosnoe, R., Muller, C., Tipton, E., Schneider, B., Hulleman, C. S., Hinojosa, C. P., et al. (2019) · Nature · doi:10.1038/s41586-019-1466-y · Cited by 1,513 · cited 1×
“This has been important in applications of the methods outlined in this paper (Yeager et al., 2019).”
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Behavioural science is unlikely to change the world without a heterogeneity revolution
Bryan, C. J., Tipton, E., & Yeager, D. S. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01143-3 · Cited by 537
Yao, L., Chu, Z., Li, S., Li, Y., Gao, J., & Zhang, A. (2021) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3444944 · Cited by 478
Causal inference and counterfactual prediction in machine learning for actionable healthcare
Prosperi, M., Guo, Y., Sperrin, M., Koopman, J. S., Min, J. S., He, X., Rich, S., Wang, M., Buchan, I. E., & Bian, J. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0197-y · Cited by 459
Causal Inference About the Effects of Interventions From Observational Studies in Medical Journals
Dahabreh, I. J., & Bibbins-Domingo, K. (2024) · JAMA · doi:10.1001/jama.2024.7741 · Cited by 242
A synergistic mindsets intervention protects adolescents from stress
Yeager, D. S., Bryan, C. J., Gross, J. J., Murray, J. S., Krettek Cobb, D., H. F. Santos, P., Gravelding, H., Johnson, M., & Jamieson, J. P. (2022) · Nature · doi:10.1038/s41586-022-04907-7 · Cited by 225
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Metadata
- Authors
- Hahn, P. R., Murray, J. S., & Carvalho, C. M.
- Venue
- Bayesian Analysis
- Publisher
- Institute of Mathematical Statistics
- Published
- 2020-09-01
- Volume
- 15
- Issue
- 3
- Pages
- 965-1056
- ISSN
- 1931-6690 (ISSN-L)
- 1936-0975
- OpenAlex
- W3004404638
- Cited by
- 304
- References
- 62
- FWCI
- 25.74
- Type
- journal article
- Language
- en
- Open access
- diamond
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- Hahn, P. R., Murray, J. S., & Carvalho, C. M. (2020). Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects (with Discussion). Bayesian Analysis. https://doi.org/10.1214/19-ba1195
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