This paper provides a review of methodological advancements in the evaluation of
heterogeneous treatment effect models based on instrumental variable (IV) methods. We …

In settings that exhibit selection on both levels and gains, marginal treatment effects (MTE)
allow us to go beyond local average treatment effects and estimate the whole distribution of …

We propose inverse probability weighted estimators for the distribution functions of the
potential outcomes under the unconfoundedness assumption and apply the inverse …

Flexible estimation of heterogeneous treatment effects lies at the heart of many statistical
applications, such as personalized medicine and optimal resource allocation. In this article …

In this paper, I study the statistical inference problem for treatment assignment policies. In
typical applications, individuals with different characteristics are expected to differ in their …

In causal inference, the joint law of a set of counterfactual random variables is generally not
identified. We show that a conservative version of the joint law-corresponding to the smallest …

Heterogeneous treatment effect models allow us to compare treatments at subgroup and
individual levels, and are of increasing popularity in applications like personalized medicine …

The triangular model is a very popular way to allow for causal inference in the presence of
endogeneity. In this model, an outcome is determined by an endogenous regressor, which …

Estimating how a treatment affects different individuals, known as heterogeneous treatment
effect estimation, is an important problem in empirical sciences. In the last few years, there …

This paper discusses the problem of estimation and inference on the effects of time-varying
treatment. We propose a method for inference on the effects treatment histories, introducing …