C-XGBoost: A tree boosting model for causal effect estimation

arXiv:2404.00751v1 Announce Type: cross
Abstract: Causal effect estimation aims at estimating the Average Treatment Effect as well as the Conditional Average Treatment Effect of a treatment to an outcome from the available data. This knowledge is important in many safety-critical domains, where it often needs to be extracted from observational data. In this work, we propose a new causal inference model, named C-XGBoost, for the prediction of potential outcomes. The motivation of our approach is to exploit the superiority of …

abstract arxiv boosting causal cs.lg data domains knowledge safety safety-critical stat.me stat.ml treatment tree type xgboost