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07/11/2023
Probing the Quantitative-Qualitative Divide in Probabilistic Reasoning
This paper explores the space of (propositional) probabilistic logical l...
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06/25/2023
Comparing Causal Frameworks: Potential Outcomes, Structural Models, Graphs, and Abstractions
The aim of this paper is to make clear and precise the relationship betw...
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03/05/2023
Finding Alignments Between Interpretable Causal Variables and Distributed Neural Representations
Causal abstraction is a promising theoretical framework for explainable ...
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01/11/2023
Causal Abstraction for Faithful Model Interpretation
A faithful and interpretable explanation of an AI model's behavior and i...
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11/22/2022
Causal Abstraction with Soft Interventions
Causal abstraction provides a theory describing how several causal model...
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11/16/2022
Holistic Evaluation of Language Models
Language models (LMs) are becoming the foundation for almost all major l...
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12/05/2021
Causal Distillation for Language Models
Distillation efforts have led to language models that are more compact a...
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12/01/2021
Inducing Causal Structure for Interpretable Neural Networks
In many areas, we have well-founded insights about causal structure that...
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11/27/2021
Is Causal Reasoning Harder than Probabilistic Reasoning?
Many tasks in statistical and causal inference can be construed as probl...
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07/18/2021
A Topological Perspective on Causal Inference
This paper presents a topological learning-theoretic perspective on caus...
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06/06/2021
Causal Abstractions of Neural Networks
Structural analysis methods (e.g., probing and feature attribution) are ...
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04/17/2020
Intention as Commitment toward Time
In this paper we address the interplay among intention, time, and belief...
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01/09/2020
Probabilistic Reasoning across the Causal Hierarchy
We propose a formalization of the three-tier causal hierarchy of associa...
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07/04/2019
On Open-Universe Causal Reasoning
We extend two kinds of causal models, structural equation models and sim...
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05/08/2018