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 …

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

A common concern when trying to draw causal inferences from observational data is that the
measured covariates are insufficiently rich to account for all sources of confounding. In …

We propose estimators based on kernel ridge regression for nonparametric causal functions
such as dose, heterogeneous and incremental response curves. The treatment and …

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 …

Existing statistical methods in causal inference often rely on the assumption that every
individual has some chance of receiving any treatment level regardless of its associated …

This supplementary document contains additional justifications and the proofs of the
theoretical results presented in the main document. All the results and notation are …

We identify the average dose–response function (ADRF) for a continuously valued error-
contaminated treatment by a weighted conditional expectation. We then estimate the …

In many real-world causal inference applications, the primary outcomes (labels) are often
partially missing, especially if they are expensive or difficult to collect. If the missingness …

Estimating treatment effects has numerous real-world applications in various fields, such as
epidemiology and political science. While much attention has been devoted to addressing …