On the consistency rule in causal inference: axiom, definition, assumption, or theorem?
In 2 recent communications, Cole and Frangakis (Epidemiology. 2009;20:3-5) and VanderWeele (Epidemiology. 2009;20:880-883) conclude that the consistency rule used in causal inference is an assumption that precludes any side-effects of treatment/exposure on the outcomes of interest. They further develop auxiliary notation to make this assumption formal and explicit. I argue that the consistency rule is a theorem in the logic of counterfactuals and need not be altered. Instead, warnings of potential side-effects should be embodied in standard modeling practices that make causal assumptions explicit and transparent.
Pearl
Judea
J
Department of Computer Science, University of California, Los Angeles, CA 90095-1596, USA. judea@cs.ucla.edu
D015984:Causality; D008962:Models, Theoretical