Often researchers find parametric models restrictive and sensitive to deviations from the
parametric specifications; semi-nonparametric models are more flexible and robust, but lead …

This paper analyzes difference-in-differences designs with a continuous treatment. We show
that treatment effect on the treated-type parameters can be identified under a generalized …

We survey the nascent literature on machine learning in the study of financial markets. We
highlight the best examples of what this line of research has to offer and recommend …

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 …

In this article, we consider identification, estimation, and inference procedures for treatment
effect parameters using Difference-in-Differences (DiD) with (i) multiple time periods,(ii) …

The last 15 years has brought forth an explosion of research on consumption-based asset
pricing as a leading contender for explaining aggregate stock market behavior. This …

This paper examines some of the recent literature on the estimation of production functions.
We focus on techniques suggested in two recent papers, Olley and Pakes (1996) and …

In many areas, practitioners seek to use observational data to learn a treatment assignment
policy that satisfies application‐specific constraints, such as budget, fairness, simplicity, or …

Many economic and causal parameters depend on nonparametric or high dimensional first
steps. We give a general construction of locally robust/orthogonal moment functions for …

Orthogonal statistical learning Page 1 The Annals of Statistics 2023, Vol. 51, No. 3, 879–908
https://doi.org/10.1214/23-AOS2258 © Institute of Mathematical Statistics, 2023 ORTHOGONAL …