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Heterogeneous Treatment Effect Estimation under Noncompliance in the Illinois Workplace Wellness Study with Bayesian Tree Ensembles

arXiv 2408.07765 ↗First posted 14 Aug 2024stat.AP stat.ME stat.MLcited by 0

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Abstract (arXiv)

Estimating varying treatment effects in randomized trials with noncompliance is inherently challenging since variation comes from two separate sources: variation in the impact itself and variation in the compliance rate. In this setting, existing flexible machine learning methods are sensitive to the weak instruments problem and can yield unstable estimates of heterogeneity when run repeatedly with different initialization or random seeds. Our main methodological contribution is to present a Bayesian Causal Forest model for binary response variables in scenarios with noncompliance. By repeatedly imputing individuals' compliance types, we can flexibly estimate heterogeneous treatment effects among compliers. Simulation studies demonstrate the usefulness of our approach when compliance and treatment effects are heterogeneous. We use this method to detect and analyze heterogeneity in the treatment effects in the Illinois Workplace Wellness Study, which not only features heterogeneous and one-sided compliance but also several binary outcomes of interest. We focus on three outcomes one year after intervention. We confirm a null effect on the presence of a chronic condition, discover meaningful heterogeneity in the impact of the intervention on metabolic parameters though the average effect is null in classical partial effect estimates, and find heterogeneity in the intervention's effect on individuals' perception of management prioritization of health and safety.

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References · 18

  1. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 597
  2. 2017Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learningcited by 475
  3. 2016Generalized Random Forestscited by 309
  4. 2015Recursive Partitioning for Heterogeneous Causal Effectscited by 254
  5. 2019Rank-normalization, folding, and localization: An improved widehatR for assessing convergence of MCMCcited by 90
  6. 2021Discussion on "Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects" by Hahn, Murray and Carvalhocited by 77
  7. 2019Estimating Treatment Effects with Causal Forests: An Applicationcited by 58
  8. 2018Efficient nonparametric causal inference with missing exposure informationcited by 10
  9. 2014Decoupling shrinkage and selection in Bayesian linear models: a posterior summary perspectivecited by 8
  10. 2022Generalized Bayesian Additive Regression Trees Models: Beyond Conditional Conjugacycited by 8
  11. 2020VCBART: Bayesian trees for varying coefficientscited by 7
  12. 2019Heterogeneous causal effects with imperfect compliance: a Bayesian machine learning approachcited by 6
6 more
  1. 2016Variable Selection in Seemingly Unrelated Regressions with Random Predictorscited by 4
  2. 2021Causal Inference with the Instrumental Variable Approach and Bayesian Nonparametric Machine Learningcited by 2
  3. 2020Decoupling Shrinkage and Selection in Gaussian Linear Factor Analysiscited by 1
  4. 2020Bounding the local average treatment effect in an instrumental variable analysis of engagement with a mobile interventioncited by 1
  5. 2024Bayesian Nonparametric Trees for Principal Causal Effectscited by 1
  6. 2024Combining BART and Principal Stratification to estimate the effect of intermediate on primary outcomes with application to estimating the effect of family planning on employment in sub-Saharan Africacited by 1

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