We develop a method to learn about treatment effects in multiple treatment models with
discrete-valued instruments. We allow selection into treatment to be governed by a general …

Multi-dimensional heterogeneity and endogeneity are important features of models with
multiple treatments. We consider a heterogeneous coefficients model where the outcome is …

This paper considers treatment effects under endogeneity with complex heterogeneity in the
selection equation. We model the outcome of an endogenous treatment as a triangular …

We revisit the classical causal inference problem of estimating the average treatment effect
in the presence of fully observed confounding variables using two-stage semiparametric …

This paper considers the evaluation of the average treatment effect of a binary endogenous
regressor on a binary outcome when one imposes a threshold crossing model on both the …

Multidimensional heterogeneity and endogeneity are important features of a wide class of
econometric models. We consider heterogenous coefficients models where the outcome is a …

Statisticians show growing interest in estimating and analyzing heterogeneity in causal
effects in observational studies. However, there usually exists a trade-off between accuracy …

We consider the estimation of treatment effects in settings when multiple treatments are
assigned over time and treatments can have a causal effect on future outcomes. We propose …

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 …

We propose the sharp identifiable bounds of the potential outcome distributions using panel
data. We allow for the possibility that statistical randomization of treatment assignments is …