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A Topological Perspective on Causal Inference
This paper presents a topological learning-theoretic perspective on causal inference by introducing a series of topologies defined on general spaces of structural causal models (SCMs).
Probabilistic Reasoning across the Causal Hierarchy
We propose a formalization of the three-tier causal hierarchy of association, intervention, and counterfactuals as a series of probabilistic logical languages.
On Open-Universe Causal Reasoning
We extend two kinds of causal models, structural equation models and simulation models, to infinite variable spaces.
Causal Modeling with Probabilistic Simulation Models
Recent authors have proposed analyzing conditional reasoning through a notion of intervention on a simulation program, and have found a sound and complete axiomatization of the logic of conditionals in this setting.
On the Conditional Logic of Simulation Models
We propose analyzing conditional reasoning by appeal to a notion of intervention on a simulation program, formalizing and subsuming a number of approaches to conditional thinking in the recent AI literature.
