Abstracting Causal Models

Authors

  • Sander Beckers Utrecht University
  • Joseph Y. Halpern Cornell University

DOI:

https://doi.org/10.1609/aaai.v33i01.33012678

Abstract

We consider a sequence of successively more restrictive definitions of abstraction for causal models, starting with a notion introduced by Rubenstein et al. (2017) called exact transformation that applies to probabilistic causal models, moving to a notion of uniform transformation that applies to deterministic causal models and does not allow differences to be hidden by the “right” choice of distribution, and then to abstraction, where the interventions of interest are determined by the map from low-level states to high-level states, and strong abstraction, which takes more seriously all potential interventions in a model, not just the allowed interventions. We show that procedures for combining micro-variables into macro-variables are instances of our notion of strong abstraction, as are all the examples considered by Rubenstein et al.

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Published

2019-07-17

How to Cite

Beckers, S., & Halpern, J. Y. (2019). Abstracting Causal Models. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 2678-2685. https://doi.org/10.1609/aaai.v33i01.33012678

Issue

Section

AAAI Technical Track: Knowledge Representation and Reasoning