Ancestor regression in linear structural equation models

Christoph Schultheiss, Peter Bühlmann

Abstract: We present a new method for causal discovery in linear structural equation models. We propose a simple trick based on statistical testing in linear models that can distinguish between ancestors and non-ancestors of any given variable. Naturally, this can then be extended to estimating the causal order among all variables. Unlike many methods, we provide explicit error control for false causal discovery, at least asymptotically. This holds true even under Gaussianity where various methods fail due to non-identifiable structures. These type I error guarantees come at the cost of reduced power. Additionally, we provide an asymptotically valid goodness of fit p-value to assess whether multivariate data stems from a linear structural equation model.