Universal Causal Inference in a Topos
Sridhar Mahadevan
摘要
In this paper, we explore the universal properties underlying causal inference by formulating it in terms of a topos . More concretely, we introduce topos causal models (TCMs), a strict generalization of the popular structural causal models (SCMs). A topos category has several properties that make it attractive: a general theory for how to combine local functions that define “independent causal mechanisms" into a consistent global function building on the theory of sheaves in a topos; a generic way to define causal interventions using a subobject classifier in a topos category; and finally, an internal logical language for causal and counterfactual reasoning that emerges from the topos itself. A striking characteristic of subobject classifiers is that they induce an intuitionistic logic, whose semantics is based on the partially ordered lattice of subobjects. We show that the underlying subobject classifier for causal inference is not Boolean in general, but forms a Heyting algebra. We define the internal Mitchell-Bénabou language, a typed local set theory, associated with causal models, and its associated Kripke-Joyal intuitionistic semantics. We prove a universal property of TCM, namely that any causal functor mapping decomposable structure to probabilistic semantics factors uniquely through a TCM representation.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
相关 Paper
- Probabilistic Reasoning Across the Causal HierarchyDuligur Ibeling, Thomas IcardAAAI 2020 · 被引用 35 次
- A Topological Perspective on Causal InferenceDuligur Ibeling, Thomas IcardNeurIPS 2021 · 被引用 12 次
- From Probability to Counterfactuals: the Increasing Complexity of Satisfiability in Pearl's Causal HierarchyJulian Dörfler, Benito van der Zander, Markus Bläser, Maciej LiskiewiczICLR 2025 · 被引用 1 次
- A Fixed-Point Approach for Causal Generative ModelingMeyer Scetbon, Joel Jennings, Agrin Hilmkil, Cheng Zhang 等ICML 2024 · 被引用 4 次
- Comparing Causal Frameworks: Potential Outcomes, Structural Models, Graphs, and AbstractionsDuligur Ibeling, Thomas IcardNeurIPS 2023 · 被引用 27 次
