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 doubly robust inference method for causal effects of continuous treatment
variables, under unconfoundedness and with nonparametric or high-dimensional nuisance …

We consider empirical measurement of equivalent variation (EV) and compensating
variation (CV) resulting from price change of a discrete good using individual‐level data …

This article studies identification, estimation, and inference of general unconditional
treatment effects models with continuous treatment under the ignorability assumption. We …

Sample selection problems arise when treatment affects both the outcome and the
researcher's ability to observe it. This paper generalizes Lee (2009) bounds for the average …

This paper proposes semi‐and nonparametric methods for disentangling the total causal
effect of a continuous treatment on an outcome variable into its natural direct effect and the …

This article studies two-step sieve M estimation of general semi/nonparametric models,
where the second step involves sieve estimation of unknown functions that may use the …

This paper introduces a generalized ps-BART model for the estimation of Average
Treatment Effect (ATE) and Conditional Average Treatment Effect (CATE) in continuous …

This paper critically examines current methodologies for evaluating models in Conditional
and Average Treatment Effect (CATE/ATE) estimation, identifying several key pitfalls in …

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 …