10 citations · 11 across the 5 of their papers we have counts for
6 papers
A Topological Sorting Criterion for Random Causal Directed Acyclic Graphs
Alexander G. Reisach, Antoine Chambaz, Gilles Blanchard +1
Random directed acyclic graphs (DAGs) based on imposing an order on Erdős-Rényi and scale free random graphs are widely used for evaluating causal discovery algorithms. We show tha…
A phase transition in Erdős-Barak random graphs
Gilles Blanchard, Nicolas Curien, Klara Krause +1
We study monotone paths in Erdős-Rényi random graphs on numbered vertices. Benjamini & Tzalik established a phase transition at for this model. We refine the…
Transforming Conditional Density Estimation Into a Single Nonparametric Regression Task
Alexander G. Reisach, Olivier Collier, Alex Luedtke +1
We propose a way of transforming the problem of conditional density estimation into a single nonparametric regression task via the introduction of auxiliary samples. This allows le…
The Case for Time in Causal DAGs
Alexander G. Reisach, Alberto Suárez, Sebastian Weichwald +1
We make the case for incorporating a notion of time into causal directed acyclic graphs (DAGs). We demonstrate that nontemporal causal DAGs are ambiguous and obstruct justification…
A Scale-Invariant Sorting Criterion to Find a Causal Order in Additive Noise Models
Alexander G. Reisach, Myriam Tami, Christof Seiler +2
Additive Noise Models (ANMs) are a common model class for causal discovery from observational data and are often used to generate synthetic data for causal discovery benchmarking.…
Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Game
Alexander G. Reisach, Christof Seiler, Sebastian Weichwald
Simulated DAG models may exhibit properties that, perhaps inadvertently, render their structure identifiable and unexpectedly affect structure learning algorithms. Here, we show th…