2.3k citations · 2.4k across the 19 of their papers we have counts for
4 papers · 1 filter
Probing the Quantitative-Qualitative Divide in Probabilistic Reasoning
Duligur Ibeling, Thomas Icard, Krzysztof Mierzewski +1
This paper explores the space of (propositional) probabilistic logical languages, ranging from a purely `qualitative' comparative language to a highly `quantitative' language invol…
Comparing Causal Frameworks: Potential Outcomes, Structural Models, Graphs, and Abstractions
Duligur Ibeling, Thomas Icard
The aim of this paper is to make clear and precise the relationship between the Rubin causal model (RCM) and structural causal model (SCM) frameworks for causal inference. Adopting…
Finding Alignments Between Interpretable Causal Variables and Distributed Neural Representations
Atticus Geiger, Zhengxuan Wu, Christopher Potts +2
Causal abstraction is a promising theoretical framework for explainable artificial intelligence that defines when an interpretable high-level causal model is a faithful simplificat…
Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretability
Atticus Geiger, Duligur Ibeling, Amir Zur +8
Causal abstraction provides a theoretical foundation for mechanistic interpretability, the field concerned with providing intelligible algorithms that are faithful simplifications…