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The finite sample performance of instrumental variable-based estimators of the Local Average Treatment Effect when controlling for covariates
This paper investigates the finite sample performance of a range of parametric, semi-parametric, and non-parametric instrumental variable estimators when controlling for a fixed set of covariates to evaluate the local average treatment effect.
Evaluating (weighted) dynamic treatment effects by double machine learning
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.
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