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Comparative Study
. 2011 May;22(3):368-77.
doi: 10.1097/EDE.0b013e3182109296.

Compound treatments and transportability of causal inference

Miguel A Hernán  1 , Tyler J VanderWeele
Affiliations
Comparative Study

Compound treatments and transportability of causal inference

Miguel A Hernán et al. Epidemiology. 2011 May.

Abstract

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.

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Figures

FIGURE 1
FIGURE 1
Randomized experiment with a compound treatment R.
FIGURE 2
FIGURE 2
Randomized experiment with a compound treatment R.
FIGURE 3
FIGURE 3
Observational study with a simple treatment A.
FIGURE 4
FIGURE 4
Observational study with a compound treatment R (treatment precedes versions).
FIGURE 5
FIGURE 5
Observational study with a compound treatment R (versions precede treatment).
See this image and copyright information in PMC

Comment in

References

    1. Lewis D. Counterfactuals. Oxford: Blackwell; 1973.
    1. Holland PW. Statistics and causal inference. J Am Stat Assoc. 1986;81:495–970.
    1. Robins JM, Greenland S. Comment on “Causal inference without counterfactuals”. J Am Stat Assoc. 2000;95:477–482.
    1. Greenland S. Causality theory for policy uses of epidemiologic measures. In: Murray CJ, Salomon JA, Mathers CD, et al., editors. Summary Measures of Population Health. Cambridge, MA: Harvard University Press/World Health Organization; 2002.
    1. Hernán MA. Invited commentary: hypothetical interventions to define causal effects: afterthought or prerequisite? Am J Epidemiol. 2005;162:618–620. - PubMed

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