Ancestor regression in linear structural equation models

Christoph Schultheiss; Peter Bühlmann

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arXiv · 2205.08925

Ancestor regression in linear structural equation models

Christoph Schultheiss·Peter Bühlmann

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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. We provide explicit error control for false causal discovery, at least asymptotically. This holds true even under Gaussianity, where other methods fail due to non-identifiable structures. These type I error guarantees come at the cost of reduced empirical power. Additionally, we provide an asymptotically valid goodness of fit p-value to assess whether multivariate data stems from a linear structural equation model.

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Christoph Schultheiss, Peter Bühlmann. 2022-05-18. Ancestor regression in linear structural equation models. https://arxiv.org/abs/2205.08925

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