Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning

arXiv:2405.04715v1 Announce Type: cross
Abstract: Statistics suffers from a fundamental problem, "the curse of endogeneity" -- the regression function, or more broadly the prediction risk minimizer with infinite data, may not be the target we wish to pursue. This is because when complex data are collected from multiple sources, the biases deviated from the interested (causal) association inherited in individuals or sub-populations are not expected to be canceled. Traditional remedies are of hindsight and restrictive in being tailored to prior …

abstract adversarial arxiv causality cs.lg data endogeneity environments function fundamental math.st multiple prediction regression risk statistics stat.me stat.ml stat.th type via