Triple/Debiased Lasso for Statistical Inference of Conditional Average Treatment Effects

arXiv:2403.03240v1 Announce Type: cross
Abstract: This study investigates the estimation and the statistical inference about Conditional Average Treatment Effects (CATEs), which have garnered attention as a metric representing individualized causal effects. In our data-generating process, we assume linear models for the outcomes associated with binary treatments and define the CATE as a difference between the expected outcomes of these linear models. This study allows the linear models to be high-dimensional, and our interest lies in consistent estimation and statistical inference …

abstract arxiv attention binary cs.lg data econ.em effects inference lasso linear process statistical stat.me stat.ml study treatment type