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.
From the Department of Computer Science, University of California, Los Angeles, CA.
Submitted 9 February 2010; accepted 23 April 2010.
Supported partially by NIH grants 1R01 LM009961–01, NSF grants IIS-0914211, and ONR grants N000–14–09–1-0665.
Correspondence: 4532 Boelter Hall, Department of Computer Science, University of California, Los Angeles, CA 90095–1596. E-mail: firstname.lastname@example.org.