From the 1 of 5 linked papers with an AI index.
5 papers
Falsifying Causal Graphs With Outlier Events
William Roy Orchard, Philipp M. Faller, Dominik Janzing
The paper introduces statistical tests that use the propagation of outlier events to falsify candidate causal graphs, providing guarantees on false positives and detection power ev…
On Different Notions of Redundancy in Conditional-Independence-Based Discovery of Graphical Models
Philipp M. Faller, Dominik Janzing
Conditional-independence-based discovery uses statistical tests to identify a graphical model that represents the independence structure of variables in a dataset. These tests, how…
From Guess2Graph: When and How Can Unreliable Experts Safely Boost Causal Discovery in Finite Samples?
Sujai Hiremath, Dominik Janzing, Philipp Faller +4
Causal discovery algorithms often perform poorly with limited samples. While integrating expert knowledge (including from LLMs) as constraints promises to improve performance, guar…
Score matching through the roof: linear, nonlinear, and latent variables causal discovery
Francesco Montagna, Philipp M. Faller, Patrick Bloebaum +2
Causal discovery from observational data holds great promise, but existing methods rely on strong assumptions about the underlying causal structure, often requiring full observabil…
Cross-validating causal discovery via Leave-One-Variable-Out
Daniela Schkoda, Philipp Faller, Patrick Blöbaum +2
We propose a new approach to falsify causal discovery algorithms without ground truth, which is based on testing the causal model on a variable pair excluded during learning the ca…