6 papers
FoundCause: Causal Discovery with Latent Confounders from Observational Data
Patrick Blöbaum, Krishnakumar Balasubramanian, Shiva Prasad Kasiviswanathan
Causal discovery from observational data remains challenging due to the need to recover directed structure and latent confounding without interventions. We propose FoundCause, an a…
Debiasing Reward Models by Representation Learning with Guarantees
Ignavier Ng, Patrick Blöbaum, Siddharth Bhandari +2
Recent alignment techniques, such as reinforcement learning from human feedback, have been widely adopted to align large language models with human preferences by learning and leve…
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…
Sequential Kernelized Independence Testing
Aleksandr Podkopaev, Patrick Blöbaum, Shiva Prasad Kasiviswanathan +1
Independence testing is a classical statistical problem that has been extensively studied in the batch setting when one fixes the sample size before collecting data. However, pract…
Causal vs. Anticausal merging of predictors
Sergio Hernan Garrido Mejia, Patrick Blöbaum, Bernhard Schölkopf +1
We study the differences arising from merging predictors in the causal and anticausal directions using the same data. In particular we study the asymmetries that arise in a simple…
Toward Falsifying Causal Graphs Using a Permutation-Based Test
Elias Eulig, Atalanti A. Mastakouri, Patrick Blöbaum +2
Understanding causal relationships among the variables of a system is paramount to explain and control its behavior. For many real-world systems, however, the true causal graph is…