Reinforcement learning (RL) is one of the most vibrant research frontiers in machine
learning and has been recently applied to solve a number of challenging problems. In this …

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) …

We revisit the classic semi‐parametric problem of inference on a low‐dimensional
parameter θ0 in the presence of high‐dimensional nuisance parameters η0. We depart from …

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 …

Off-policy evaluation (OPE) in reinforcement learning allows one to evaluate novel decision
policies without needing to conduct exploration, which is often costly or otherwise infeasible …

In this paper, we provide efficient estimators and honest confidence bands for a variety of
treatment effects including local average (LATE) and local quantile treatment effects (LQTE) …

Estimation and inference on causal parameters is typically reduced to a generalized method
of moments problem, which involves auxiliary functions that correspond to solutions to a …

Instrumental variables are widely used in applied econometrics to achieve identification and
carry out estimation and inference in models that contain endogenous explanatory …