Phillip Heiler from Aarthus will present:

Title:

"Estimating Heterogeneous Bounds for Treatment Effects under Sample Selection and Non-response"

 Abstract:

In this paper we propose a method for nonparametric identification and estimation

of heterogeneous bounds for causal effect parameters in general sample selection

models where the initial treatment can affect whether an post-intervention outcome

is observed or not. The original treatment selection can be confounded by observable

covariates while the outcome selection can be affected by both observables

and unobservables. The method provides functional estimates of conditional effect

bounds dependent on pre-treatment characteristics. It allows for

conducting valid statistical inference on the unidentified conditional effect curves.

We use a flexible semiparametric de-biased machine learning approach that can

accommodate flexible functional forms and high-dimensional set of observed confounding

variables in both treatment and outcome selection process.