Christof Seiler — accepted AI-conference papers

2023

A Scale-Invariant Sorting Criterion to Find a Causal Order in Additive Noise Models

Alexander Gilbert Reisach, Myriam Tami, Christof Seiler, Antoine Chambaz, Sebastian Weichwald

NeurIPS 2023poster

Additive Noise Models (ANMs) are a common model class for causal discovery from observational data. Due to a lack of real-world data for which an underlying ANM is known, ANMs with randomly sampled parameters are commonly used to simulate data for the evaluation of causal discovery algorithms. While…

Cited by 22 Source ↗PDF ↗code ↗Scholar ↗

2021

Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy to Game

Alexander Gilbert Reisach, Christof Seiler, Sebastian Weichwald

NeurIPS 2021poster

Simulated DAG models may exhibit properties that, perhaps inadvertently, render their structure identifiable and unexpectedly affect structure learning algorithms. Here, we show that marginal variance tends to increase along the causal order for generically sampled additive noise models. We introduc…

Cited by 179 Source ↗PDF ↗code ↗Scholar ↗