From the 1 of 10 linked papers with an AI index.
10 papers
Verifying formulas for interventional distributions
Francesco Freni, Leonard Henckel, Sebastian Weichwald
The paper defines the verification problem for causal graphical models—checking whether a specific observational formula correctly identifies a target interventional distribution—a…
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 ErdÅs-Rényi and scale free random graphs are widely used for evaluating causal discovery algorithms. We show t…
Causal Foundations of Collective Agency
Frederik Hytting Jørgensen, Sebastian Weichwald, Lewis Hammond
A key challenge for the safety of advanced AI systems is the possibility that multiple simpler agents might inadvertently form a collective agent with capabilities and goals distin…
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…
Identifying Causal Effects Using a Single Proxy Variable
Silvan Vollmer, Niklas Pfister, Sebastian Weichwald
Unobserved confounding is a key challenge when estimating causal effects from a treatment on an outcome in scientific applications. In this work, we assume that we observe a single…
Embracing Discrete Search: A Reasonable Approach to Causal Structure Learning
Marcel Wienöbst, Leonard Henckel, Sebastian Weichwald
We present FLOP (Fast Learning of Order and Parents), a score-based causal discovery algorithm for linear models. It pairs fast parent selection with iterative Cholesky-based score…