Evaluating (weighted) dynamic treatment effects by double machine learning

no code implementations • 1 Dec 2020 • Hugo Bodory, Martin Huber, Lukáš Lafférs

We consider evaluating the causal effects of dynamic treatments, i. e. of multiple treatment sequences in various periods, based on double machine learning to control for observed, time-varying covariates in a data-driven way under a selection-on-observables assumption.

BIG-bench Machine Learning

Double machine learning for sample selection models

no code implementations • 30 Nov 2020 • Michela Bia, Martin Huber, Lukáš Lafférs

This paper considers the evaluation of discretely distributed treatments when outcomes are only observed for a subpopulation due to sample selection or outcome attrition.

BIG-bench Machine Learning