This paper synthesizes recent advances in the econometrics of difference-in-differences
(DiD) and provides concrete recommendations for practitioners. We begin by articulating a …

This article proposes doubly robust estimators for the average treatment effect on the treated
(ATT) in difference-in-differences (DID) research designs. In contrast to alternative DID …

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

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

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

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 …

Simultaneous confidence bands are versatile tools for visualizing estimation uncertainty for
parameter vectors, such as impulse response functions. In linear models, it is known that that …

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 …

Minimax rates for heterogeneous causal effect estimation Page 1 The Annals of Statistics
2024, Vol. 52, No. 2, 793–816 https://doi.org/10.1214/24-AOS2369 © Institute of Mathematical …