Regularization in regression reduces confounding effects in causal models even in the population limit, and a causal generalization bound limits the error of treating non-linear regressions as causal under a symmetric confounder model.
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Causal Regularization
Regularization in regression reduces confounding effects in causal models even in the population limit, and a causal generalization bound limits the error of treating non-linear regressions as causal under a symmetric confounder model.