Ill-defined causal questions present serious problems for observational studies—problems that are largely unappreciated. This paper extends the usual counterfactual framework to consider causal questions about compound treatments for which there are many possible implementations (for example, “prevention of obesity”). We describe the causal effect of compound treatments and their identifiability conditions, with a special emphasis on the consistency condition. We then discuss the challenges of using the estimated effect of a compound treatment in one study population to inform decisions in the same population and in other populations. These challenges arise because the causal effect of compound treatments depends on the distribution of the versions of treatment in the population. Such causal effects can be unpredictable when the versions of treatment are unknown. We discuss how such issues of “transportability” are related to the consistency condition in causal inference. With more carefully framed questions, the results of epidemiologic studies can be of greater value to decision-makers.
From the aDepartment of Epidemiology, Harvard School of Public Health, Boston, MA; bHarvard-MIT Division of Health Sciences and Technology, Boston, MA; and cDepartment of Biostatistics, Harvard School of Public Health, Boston, MA.
Submitted May 26, 2010; accepted 26 October 2010; posted 11 March 2011.
Supported by NIH grants HL080644, AI073127, and HD060696.
Editors' note: A commentary on this article appears on page 378.
Correspondence: Miguel Hernán, Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115. E-mail: miguel@email@example.com.