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 …

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

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

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 …

This paper considers estimation and inference for heterogeneous counterfactual effects with
high‐dimensional data. We propose a novel robust score for debiased estimation of the …

Most works in causal inference focus on binary treatments where one estimates a single
treatment-versus-control effect. When treatment is continuous, one must estimate a curve …

We study regression discontinuity designs in which many predetermined covariates,
possibly much more than the number of observations, can be used to increase the precision …

Offline reinforcement learning, wherein one uses off-policy data logged by a fixed behavior
policy to evaluate and learn new policies, is crucial in applications where experimentation is …

In this paper, we introduce and prove asymptotic normality for a new nonparametric
estimator of continuous treatment effects. Specifically, we estimate the average dose …

Many empirical examples of regression discontinuity (RD) designs concern a continuous
treatment variable, but the theoretical aspects of such models are less studied. This study …