Causal discovery through Pearson risk invariance

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• Veronica Vinciotti (University of Trento)
• Thursday 05 March 2026, 10:30-11:15
• Seminar Room 1, Newton Institute.

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CIFW02 - Causal identification and discovery

Prediction invariance of causal models under heterogeneous settings has been exploited by a number of recent methods for causal discovery, typically focussing on recovering the causal parents of a target variable of interest. Existing methods require observational data from a number of sufficiently different environments, which is rarely available. In this work, we consider a structural equation model where the target variable is described by a generalized linear model conditional on its parents. Under this setting, we characterize the causal model via Pearson risk invariance, leading to a computational strategy for searching the causal model among all possible models. Crucially, for generalized linear models with a known dispersion parameter, such as Poisson and logistic regression, the causal model can be identified from a single data environment. Applications range from classical Poisson/logistic regression association studies, now seen through a causal lens, to relational event models for dynamic network data. The method is implemented in the R package causalreg.

This talk is part of the Isaac Newton Institute Seminar Series series.

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