Limits of Approximating the Median Treatment Effect

arXiv:2403.10618v1 Announce Type: new
Abstract: Average Treatment Effect (ATE) estimation is a well-studied problem in causal inference. However, it does not necessarily capture the heterogeneity in the data, and several approaches have been proposed to tackle the issue, including estimating the Quantile Treatment Effects. In the finite population setting containing $n$ individuals, with treatment and control values denoted by the potential outcome vectors $\mathbf{a}, \mathbf{b}$, much of the prior work focused on estimating median$(\mathbf{a}) -$ median$(\mathbf{b})$, where median($\mathbf x$) denotes …

abstract arxiv causal causal inference cs.ai cs.ds cs.lg data econ.em effects however inference issue population quantile stat.me treatment type