Primary Submission Category: Heterogeneous Treatment Effects
Flexible estimation of heterogeneous area-specific causal treatment effects
Authors: Katarzyna Reluga, Dehan Kong, Setareh Ranjbar, Nicola Salvati, Mark van der Laan,
Presenting Author: Katarzyna Reluga*
Small area estimation (SAE) methodology is popular for estimating parameters at the level of subpopulations with small sample sizes. It is often of interest for statistical offices to quantify the effect of an intervention at the small area level, for example, the effect of a new policy targeting unemployment across counties in a region. We develop new procedures for the estimation of heterogeneous area-specific average treatment effects at the small area level in observational studies. In particular, we use nonparametric machine learning methods which are ubiquitous in causal inference as they excel in terms of modelling flexibility and predictive abilities. We compare the empirical performance of our new estimators with the parametric alternatives.