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

The need to evaluate treatment effectiveness is ubiquitous in most of empirical science, and
interest in flexibly investigating effect heterogeneity is growing rapidly. To do so, a multitude …

We propose strategies to estimate and make inference on key features of heterogeneous
effects in randomized experiments. These key features include best linear predictors of the …

Heterogeneous effect estimation is crucial in causal inference, with applications across
medicine and social science. Many methods for estimating conditional average treatment …

We investigate the finite-sample performance of causal machine learning estimators for
heterogeneous causal effects at different aggregation levels. We employ an empirical Monte …

We consider a deep learning model for classifying high-dimensional data and seek to
achieve optimal evaluation accuracy and robustness based on multicriteria decision-making …

In this paper, we review recent advances in statistical methods for the evaluation of the
heterogeneity of treatment effects (HTE), including subgroup identification and estimation of …

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

We propose a nonparametric inference method for causal effects of continuous treatment
variables, under unconfoundedness and nonparametric or high-dimensional nuisance …

For treatment effects—one of the core issues in modern econometric analysis—prediction
and estimation are two sides of the same coin. As it turns out, machine learning methods are …