9 citations · 12 across the 4 of their papers we have counts for
4 papers
On the Computational Efficiency of Bayesian Additive Regression Trees: An Asymptotic Analysis
Yan Shuo Tan, Omer Ronen, Theo Saarinen +1
Bayesian Additive Regression Trees (BART) is a popular Bayesian non-parametric regression model that is commonly used in causal inference and beyond. Its strong predictive performa…
A Mixing Time Lower Bound for a Simplified Version of BART
Omer Ronen, Theo Saarinen, Yan Shuo Tan +2
Bayesian Additive Regression Trees (BART) is a popular Bayesian non-parametric regression algorithm. The posterior is a distribution over sums of decision trees, and predictions ar…
Nonparametric identification is not enough, but randomized controlled trials are
P. M. Aronow, James M. Robins, Theo Saarinen +2
We argue that randomized controlled trials (RCTs) are special even among settings where average treatment effects are identified by a nonparametric unconfoundedness assumption. Thi…
Linear Aggregation in Tree-based Estimators
Sören R. Künzel, Theo F. Saarinen, Edward W. Liu +1
Regression trees and their ensemble methods are popular methods for nonparametric regression: they combine strong predictive performance with interpretable estimators. To improve t…