From Programs to Causal Models

Abstract

The purpose of the present contribution is to explore the consequences of building causal models out of programs, and to argue that doing so has advantages for the semantics of subjunctive conditionals and of causal language. We establish basic results about expressivity and give examples to show both the power of the framework and the ways in which it differs from more familiar causal frameworks such as structural equation models.