Modeling heterogeneous treatment effects in large-scale experiments using Bayesian Additive Regression Trees

By Donald P. Green, H. Kern. 2010

Subfields: Probabilistic & Bayesian Methods, Causal Inference & Discovery

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Citations in the corpus

Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) (2018, methodology): “Available: https://arxiv.org/abs/1611.07429 [108] D. P. Green and H. L. Kern, ‘‘Modeling heterogeneous treatment effects in large-scale experiments using Bayesian additive regression trees,’’ in Proc.”
Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation (2013, background): “Many supervised learning models applied across a number of disciplines have been better understood thanks to PDPs. Green and Kern (2010) use PDPs to understand the relationship between predictors and the conditional average treatment eﬀect for a voter mobilization experiment, with the predictions…”
The Econometrics of Randomized Experiments (2016, methodology): “Taking the Bayesian perspective, Green and Kern (2012) and Hill (2011) have proposed the use of forest-based algorithms for estimating heterogeneous treatment effects. These papers use the Bayesian additive regression tree method of Chipman et al (2010), and report posterior credible intervals obtai”
Estimating treatment effect heterogeneity in randomized program evaluation (2013, methodology): “…Gail and Simon (1985), Davison (1992), Crump et al. (2008)], and (4) generalizing causal effect estimates obtained from an experimental sample to a target population [e.g., Frangakis (2009), Cole and Stuart (2010), Hartman, Grieve and Sekhon (2010), Green and Kern (2010b), Stuart et al. (2011)].”
Using Causal Inference to Estimate What-if Outcomes for Targeting Treatments (2016, methodology): “It has been successfully used in a number of causal inference tasks [Green and Kern, 2010, Hill, 2011] and was one of the winning approaches at the 2016 Atlantic Causal Inference Conference Competition [Dorie et al., 2016] for estimating treatment response.”
Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence (2023, background): “…the associative effects of predictors on the conditional average treatment outcome derived from a voter mobilisation experiment, Green and Kern [131] Elith et al. [132] made major contributions outside of the research area by replacing stochastic gradient boosting with PDP to comprehend how…”