Search Results for author:
Found 11 papers, 5 papers with code
Feature selection in stratification estimators of causal effects: lessons from potential outcomes, causal diagrams, and structural equations
What is the ideal regression (if any) for estimating average causal effects?
Stochastic Tree Ensembles for Estimating Heterogeneous Effects
Determining subgroups that respond especially well (or poorly) to specific interventions (medical or policy) requires new supervised learning methods tailored specifically for causal inference.
Statistical Aspects of SHAP: Functional ANOVA for Model Interpretation
We use this connection to show that challenges in SHAP approximations largely relate to the choice of a feature distribution and the number of $2^p$ ANOVA terms estimated.
Local Gaussian process extrapolation for BART models with applications to causal inference
Despite this success, standard implementations of BART typically provide inaccurate prediction and overly narrow prediction intervals at points outside the range of the training data.
Semi-supervised learning and the question of true versus estimated propensity scores
According to this formulation, large unlabeled data sets could be used to estimate a high dimensional propensity function and causal inference using a much smaller labeled data set could proceed via weighted estimators using the learned propensity scores.
Stochastic tree ensembles for regularized nonlinear regression
This paper develops a novel stochastic tree ensemble method for nonlinear regression, which we refer to as XBART, short for Accelerated Bayesian Additive Regression Trees.
XBART: Accelerated Bayesian Additive Regression Trees
Bayesian additive regression trees (BART) (Chipman et.
A Survey of Learning Causality with Data: Problems and Methods
This work considers the question of how convenient access to copious data impacts our ability to learn causal effects and relations.
Efficient sampling for Gaussian linear regression with arbitrary priors
This paper develops a slice sampler for Bayesian linear regression models with arbitrary priors.
Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects
This paper develops a semi-parametric Bayesian regression model for estimating heterogeneous treatment effects from observational data.
Methodology
A Structural Approach to Coordinate-Free Statistics
We construct a stochastic version of the OLS estimator, which is a continuous disintegration exactly for the class of "uncorrelated implies independent" (UII) measures.
