36 citations · 51 across the 10 of their papers we have counts for
10 papers
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…
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…
Beyond Single-Feature Importance with ICECREAM
Michael Oesterle, Patrick Blöbaum, Atalanti A. Mastakouri +1
Which set of features was responsible for a certain output of a machine learning model? Which components caused the failure of a cloud computing application? These are just two exa…
Manifold Restricted Interventional Shapley Values
Muhammad Faaiz Taufiq, Patrick Blöbaum, Lenon Minorics
Shapley values are model-agnostic methods for explaining model predictions. Many commonly used methods of computing Shapley values, known as off-manifold methods, rely on model eva…
Modeling Causal Mechanisms with Diffusion Models for Interventional and Counterfactual Queries
Patrick Chao, Patrick Blöbaum, Sapan Patel +1
We consider the problem of answering observational, interventional, and counterfactual queries in a causally sufficient setting where only observational data and the causal graph a…
Testing Granger Non-Causality in Panels with Cross-Sectional Dependencies
Lenon Minorics, Caner Turkmen, David Kernert +3
This paper proposes a new approach for testing Granger non-causality on panel data. Instead of aggregating panel member statistics, we aggregate their corresponding p-values and sh…
Analysis of cause-effect inference by comparing regression errors
Patrick Blöbaum, Dominik Janzing, Takashi Washio +2
We address the problem of inferring the causal direction between two variables by comparing the least-squares errors of the predictions in both possible directions. Under the assum…
Estimation of interventional effects of features on prediction
Patrick Blöbaum, Shohei Shimizu
The interpretability of prediction mechanisms with respect to the underlying prediction problem is often unclear. While several studies have focused on developing prediction models…
Error Asymmetry in Causal and Anticausal Regression
Patrick Blöbaum, Takashi Washio, Shohei Shimizu
It is generally difficult to make any statements about the expected prediction error in an univariate setting without further knowledge about how the data were generated. Recent wo…