Discussion on "Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects" by Hahn, Murray and Carvalho

Abstract

Hahn et al. (2020) offers an extensive study to explicate and evaluate the performance of the BCF model in different settings and provides a detailed discussion about its utility in causal inference. It is a welcomed addition to the causal machine learning literature. I will emphasize the contribution of the BCF model to the field of causal inference through discussions on two topics: 1) the difference between the PS in the BCF model and the Bayesian PS in a Bayesian updating approach, 2) an alternative exposition of the role of the PS in outcome modeling based methods for the estimation of causal effects. I will conclude with comments on avenues for future research involving BCF that will be important and much needed in the era of Big data.

References in corpus (8)

• BART: Bayesian additive regression trees
• Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning
• Estimating treatment effect heterogeneity in randomized program evaluation
• Estimating individual treatment effect: generalization bounds and algorithms
• Quasi-Oracle Estimation of Heterogeneous Treatment Effects
• Assessing lack of common support in causal inference using Bayesian nonparametrics: Implications for evaluating the effect of breastfeeding on children's cognitive outcomes
• XBART: Accelerated Bayesian Additive Regression Trees
• Gaussian Process Mixtures for Estimating Heterogeneous Treatment Effects

Cited by in corpus (28)

• Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence
• Estimating heterogeneous survival treatment effect in observational data using machine learning
• A New Spatial Count Data Model with Bayesian Additive Regression Trees for Accident Hot Spot Identification
• Bayesian Additive Regression Trees with Model Trees
• Estimating Perinatal Critical Windows of Susceptibility to Environmental Mixtures via Structured Bayesian Regression Tree Pairs
• Modern approaches for evaluating treatment effect heterogeneity from clinical trials and observational data
• Experimental Evaluation of Individualized Treatment Rules
• Targeting customers under response-dependent costs
• A Bayesian Machine Learning Approach for Estimating Heterogeneous Survivor Causal Effects: Applications to a Critical Care Trial
• Estimating a Causal Exposure Response Function with a Continuous Error-Prone Exposure: A Study of Fine Particulate Matter and All-Cause Mortality
• On Inductive Biases for Heterogeneous Treatment Effect Estimation
• Causal Effect Inference for Structured Treatments
• SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data
• Methodological considerations for novel approaches to covariate-adjusted indirect treatment comparisons
• A Groupwise Approach for Inferring Heterogeneous Treatment Effects in Causal Inference
• Assisting Clinical Decisions for Scarcely Available Treatment via Disentangled Latent Representation
• From controlled to undisciplined data: estimating causal effects in the era of data science using a potential outcome framework
• Accounting for shared covariates in semi-parametric Bayesian additive regression trees
• Estimating Heterogeneous Exposure Effects in the Case-Crossover Design using BART
• A flexible Bayesian g-formula for causal survival analyses with time-dependent confounding
• Estimating Heterogeneous Causal Effects of High-Dimensional Treatments: Application to Conjoint Analysis
• Doing Great at Estimating CATE? On the Neglected Assumptions in Benchmark Comparisons of Treatment Effect Estimators
• Bayesian Probabilistic Numerical Integration with Tree-Based Models
• Causal inference and machine learning approaches for evaluation of the health impacts of large-scale air quality regulations
• Improved inference for doubly robust estimators of heterogeneous treatment effects
• Estimating Potential Outcome Distributions with Collaborating Causal Networks
• Hierarchical Bayesian Bootstrap for Heterogeneous Treatment Effect Estimation
• Horseshoe Forests for High-Dimensional Causal Survival Analysis