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stat.ML2026
Falsifying Causal Graphs With Outlier Events
William Roy Orchard, Philipp M. Faller, Dominik Janzing
True causal relationships are rarely known, and inferring causal graphs from data is hard. A fundamental challenge is how to assess whether a given causal graph is good in the abse…
stat.ML2024
Cross-validating causal discovery via Leave-One-Variable-Out
Daniela Schkoda, Philipp Faller, Patrick Blöbaum +1
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…
stat.ML2024
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…