A General Causal Inference Framework for Cross-Sectional Observational Data

arXiv:2404.18197v1 Announce Type: cross
Abstract: Causal inference methods for observational data are highly regarded due to their wide applicability. While there are already numerous methods available for de-confounding bias, these methods generally assume that covariates consist solely of confounders or make naive assumptions about the covariates. Such assumptions face challenges in both theory and practice, particularly when dealing with high-dimensional covariates. Relaxing these naive assumptions and identifying the confounding covariates that truly require correction can effectively enhance the practical significance …

abstract arxiv assumptions bias causal causal inference challenges confounding cs.ai cs.lg data face framework general inference stat.me type while