This paper reviews, applies, and extends recently proposed methods based on double
machine learning (DML) with a focus on programme evaluation under unconfoundedness …

This paper provides estimation and inference methods for the best linear predictor
(approximation) of a structural function, such as conditional average structural and treatment …

Given the unconfoundedness assumption, we propose new nonparametric estimators for the
reduced dimensional conditional average treatment effect (CATE) function. In the first stage …

Interest in evaluating the effects of continuous treatments has been on the rise recently. To
facilitate the estimation of causal effects in this setting, the present paper introduces entropy …

Estimating heterogeneous treatment effects (HTE) from observational studies is rising in
importance due to the widespread accumulation of data in many fields. Due to the selection …

This paper provides estimation and inference methods for conditional average treatment
effects (CATE) characterized by a high‐dimensional parameter in both homogeneous cross …

Identifying causal effects from observational data is a pervasive challenge found throughout
the empirical sciences. Very general methods have been developed to decide the …

We develop methodology for estimation and inference using machine learning to enrich
economic models. Our framework takes a standard economic model and recasts the …

Adversarial examples derived from deliberately crafted perturbations on visual inputs can
easily harm decision process of deep neural networks. To prevent potential threats, various …

The analysis of causal effects is at the heart of empirical research in economics, political
science, the biomedical sciences, and beyond. To evaluate and design policies …