Is Causal Reasoning Harder Than Probabilistic Reasoning?

Review of Symbolic Logic 17 (1):106-131 (2024)
  Copy   BIBTEX

Abstract

Many tasks in statistical and causal inference can be construed as problems of entailment in a suitable formal language. We ask whether those problems are more difficult, from a computational perspective, for causal probabilistic languages than for pure probabilistic (or “associational”) languages. Despite several senses in which causal reasoning is indeed more complex—both expressively and inferentially—we show that causal entailment (or satisfiability) problems can be systematically and robustly reduced to purely probabilistic problems. Thus there is no jump in computational complexity. Along the way we answer several open problems concerning the complexity of well-known probability logics, in particular demonstrating the ${\exists \mathbb {R}}$ -completeness of a polynomial probability calculus, as well as a seemingly much simpler system, the logic of comparative conditional probability.

Other Versions

No versions found

Links

PhilArchive

External links

Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library

Similar books and articles

Analytics

Added to PP
2022-05-20

Downloads
695 (#39,343)

6 months
198 (#17,628)

Historical graph of downloads
How can I increase my downloads?

Author Profiles

Milan Mossé
University of California, Berkeley
Thomas Icard
Stanford University