From the 1 of 10 linked papers with an AI index.
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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…
Unfair Utilities and First Steps Towards Improving Them
Frederik Hytting Jørgensen, Sebastian Weichwald, Jonas Peters
Many fairness criteria constrain the policy or choice of predictors, which can have unwanted consequences, in particular, when optimizing the policy under such constraints. Here, w…
All or None: Identifiable Linear Properties of Next-token Predictors in Language Modeling
Emanuele Marconato, Sébastien Lachapelle, Sebastian Weichwald +1
We analyze identifiability as a possible explanation for the ubiquity of linear properties across language models, such as the vector difference between the representations of "eas…
What is causal about causal models and representations?
Frederik Hytting Jørgensen, Luigi Gresele, Sebastian Weichwald
Causal Bayesian networks are 'causal' models since they make predictions about interventional distributions. To connect such causal model predictions to real-world outcomes, we mus…