Model-based causal feature selection for general response types

arXiv:2309.12833v3 Announce Type: replace-cross
Abstract: Discovering causal relationships from observational data is a fundamental yet challenging task. Invariant causal prediction (ICP, Peters et al., 2016) is a method for causal feature selection which requires data from heterogeneous settings and exploits that causal models are invariant. ICP has been extended to general additive noise models and to nonparametric settings using conditional independence tests. However, the latter often suffer from low power (or poor type I error control) and additive noise models …

abstract arxiv causal data exploits feature feature selection general math.st prediction relationships stat.me stat.ml stat.th type types