Bayesian Nonparametric Modeling for Causal Inference

By Jennifer L. Hill. 2011

1,535 citations recorded by the source corpus.

Subfields: Statistics and Probability, Probabilistic & Bayesian Methods, Causal Inference & Discovery

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Citations in the corpus

Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders (2019, methodology): “Most of the previous methods for performing causal inference have focused on the static setting (Hill, 2011; Wager & Athey, 2017; Alaa & van der Schaar, 2017; Yoon et al., 2018), and less attention has been given to the time series setting.”
Deep-Treat: Learning Optimal Personalized Treatments From Observational Data Using Neural Networks (2018, methodology): “The semi-simulated dataset in (Hill 2011; Johansson, Shalit, and Sontag 2016) is based on covariates from a real randomized experiment that evaluated the impact of the IHDP on the subjects’ IQ scores at the age of three: selection bias is introduced by removing a subset of the treated population.”
Metalearners for estimating heterogeneous treatment effects using machine learning (2017, methodology): “This metaalgorithm has been studied with Bayesian additive regression trees (BARTs) (5, 6) and regression trees (4) as the base learners.”
Generalized random forests (2016, methodology): “…considerable attention in the recent literature: Athey and Imbens (2016) and Su et al. (2009) develop tree-based methods for subgroup analysis, Hill (2011) studies treatment effect estimation via Bayesian additive regression trees (Chipman, George and McCulloch, 2010), and Wager and Athey…”
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.”
Deep Counterfactual Networks with Propensity-Dropout (2017, methodology): “Outcomes are simulated based on the data generation process designated as the “Response Surface B” setting in (Hill, 2012).”
BART: Bayesian Additive Regression Trees (2008, methodology): “Finally, Hill & McCulloch (2007) apply BART to a counterfactual causality analysis for assessing the effectiveness of a job training program based on observational data.”
Learning Representations for Counterfactual Inference (2016, methodology): “For details, see Hill (2011).”
Heterogeneous Treatment Effects in Digital Experimentation (2014, methodology): “For example, Hill (2011) applies Bayesian additive regression trees (BART; Chipman et al., 2010) to obtain posterior distributions for tree-based regression in each treatment group.”
Simple Rules for Complex Decisions (2017, methodology): “Here we take a straightforward and popular statistical approach to estimating V π : response surface modeling [15].”
Automated versus Do-It-Yourself Methods for Causal Inference: Lessons Learned from a Data Analysis Competition (2017, methodology): “A less conventional measure of performance is the PEHE (precision in estimation of heterogeneous effects) (Hill, 2011).”
Quasi-oracle estimation of heterogeneous treatment effects (2017, methodology): “Some notable recent advances include proposals based on the lasso (Imai and Ratkovic, 2013), recursive partitioning (Athey and Imbens, 2016; Su et al., 2009), BART (Hahn, Murray, and Carvalho, 2017; Hill, 2011), random forests (Athey, Tibshirani, and Wager, 2018; Wager and Athey, 2017), boosting (Po”
Journal of the American Statistical Association Using Mixed Integer Programming for Matching in an Observational Study of Kidney Failure after Surgery Using Mixed Integer Programming for Matching in an Observational Study of Kidney Failure after Surgery (neutral): “The problem is that this iterative process usually involves a considerable amount of guesswork, and it is common to see that balancing some covariates worsens the balance of other covariates (Diamond and Sekhon 2005; Hill 2011; Iacus, King, and Porro 2012).”
Estimating Individual Treatment Effect in Observational Data Using Random Forest Methods (2017, methodology): “We include it, however, because of its reported success in applications to causal inference (Hill 2011).”
Optimal Prescriptive Trees (2019, methodology): “This data set is based on the Infant Health Development Program and was compiled by Hill (2011).”