313 citations · 541 across the 23 of their papers we have counts for
29 papers
Locally Equivalent Weights for Multilevel Regression and Poststratification
Ryan Giordano, Alice Cima, Jared Murray +2
Multilevel regression and poststratification (MrP) has become a workhorse method for estimating population quantities from non-probability surveys, and is the primary model-based a…
Bayesian Additive Distribution Regression
Antonio R. Linero, Soumyabrata Bose, Jared Murray
Distribution regression, where the goal is to predict a scalar response from a distribution-valued predictor, arises naturally in settings where observations are grouped and outcom…
StochTree: BART-based modeling in R and Python
Andrew Herren, P. Richard Hahn, Jared Murray +1
stochtree is a C++ library for Bayesian tree ensemble models such as BART and Bayesian Causal Forests (BCF), as well as user-specified variations. Unlike previous BART packages, st…
Forest Kernel Balancing Weights: Outcome-Guided Features for Causal Inference
Andy A. Shen, Eli Ben-Michael, Avi Feller +2
While balancing covariates between groups is central for observational causal inference, selecting which features to balance remains a challenging problem. Kernel balancing is a pr…
Density Regression with Bayesian Additive Regression Trees
Vittorio Orlandi, Jared Murray, Antonio Linero +1
Flexibly modeling how an entire density changes with covariates is an important but challenging generalization of mean and quantile regression. While existing methods for density r…
Discussion on "Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects" by Hahn, Murray and Carvalho
Liangyuan Hu
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 i…