Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Original Article
  • Published:

Does obesity shorten life? The importance of well-defined interventions to answer causal questions


Many observational studies have estimated a strong effect of obesity on mortality. In this paper, we explicitly define the causal question that is asked by these studies and discuss the problems associated with it. We argue that observational studies of obesity and mortality violate the condition of consistency of counterfactual (potential) outcomes, a necessary condition for meaningful causal inference, because (1) they do not explicitly specify the interventions on body mass index (BMI) that are being compared and (2) different methods to modify BMI may lead to different counterfactual mortality outcomes, even if they lead to the same BMI value in a given person. Besides precluding the estimation of unambiguous causal effects, this violation of consistency affects the ability to address two additional conditions that are also necessary for causal inference: exchangeability and positivity. We conclude that consistency violations not only preclude the estimation of well-defined causal effects but also compromise our ability to estimate ill-defined causal effects.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Similar content being viewed by others


  1. Allison DB, Fontaine KR, Manson JE, Stevens J, VanItallie TB . Annual deaths attributable to obesity in the United States. J Am Med Assoc 1999; 282: 1530–1538.

    Article  CAS  Google Scholar 

  2. Mokdad AH, Marks JS, Stroup DF, Gerberding JL . Actual causes of death in the United States, 2000. J Am Med Assoc 2004; 291: 1238–1245.

    Article  Google Scholar 

  3. Mokdad AH, Marks JS, Stroup DF, Gerberding JL . Correction: actual causes of death in the United States, 2000. J Am Med Assoc 2005; 293: 293–294.

    CAS  Google Scholar 

  4. Flegal KM, Graubard BI, Williamson DF, Gail MH . Excess deaths associated with underweight, overweight, and obesity. J Am Med Assoc 2005; 293: 1861–1867.

    Article  CAS  Google Scholar 

  5. Hernán MA . A definition of causal effect for epidemiological research. J Epidemiol Commun Health 2004; 58: 265–271.

    Article  Google Scholar 

  6. Hernán MA, Robins JM . Estimating causal effects from epidemiological data. J Epidemiol Commun Health 2006; 60: 578–586.

    Article  Google Scholar 

  7. Robins JM, Greenland S . Comment on ‘Causal inference without counterfactuals’ by AP Dawid. J Am Stat Assoc 2000; 95: 431–435.

    Google Scholar 

  8. Hernán MA . Invited commentary: hypothetical interventions to define causal effects: afterthought or prerequisite? Am J Epidemiol 2005; 162: 618–620.

    Article  Google Scholar 

  9. Greenland S, Rothman KJ . Measures of effect and measures of association. In: Rothman KJ, Greenland S (eds). Modern Epidemiology, 2nd edn. Lippincott-Raven: Philadelphia, 1998, pp 47–64.

    Google Scholar 

  10. Greenland S . Epidemiologic measures and policy formulation: lessons from potential outcomes (with discussion). Emerging Themes Epidemiol 2005; 2: 5.

    Article  Google Scholar 

  11. Holland PW . Statistics and causal inference. J Am Stat Assoc 1986; 81: 945–961.

    Article  Google Scholar 

  12. Robins JM, Hernán MA . Estimation of the causal effects of time-varying exposures. In: Fitzmaurice G, Davidian M, Verbeke G, Molenberghs G (eds). Advances in Longitudinal Data Analysis. Chapman & Hall/CRC Press: New York, 2008 (in press).

    Google Scholar 

Download references


We thank Sander Greenland, Sonia Hernández-Díaz and Karen Steinberg for their detailed comments and expert advice. This work was supported by NIH Grant R01 HL080644.

Author information

Authors and Affiliations


Corresponding author

Correspondence to M A Hernán.



Formal definitions

We say that a counterfactual outcome Ya is consistent with the actual or realized outcome Y if Ya=Y when the subject received exposure level A=a.

Exchangeability means that any counterfactual outcome under any treatment level a is independent of the treatment actually received A, which is written symbolically as YaA. Randomization of the treatment is expected to result in exchangeability. Stratified randomization, in which the probability of receiving treatment varies by levels of the variables in L, is expected to result in conditional exchangeability within levels L, which is written as YaAL.

For discrete treatment A and covariates L, the positivity condition is written as Pr[A=aL=l]>0 if Pr[L=l]≠0. In general, positivity is written as fAL(al)>0 if fL(l)≠0, where fXZ(xz) is the conditional density function of the random variable X evaluated at the value x given the random variable Z evaluated at the value z.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hernán, M., Taubman, S. Does obesity shorten life? The importance of well-defined interventions to answer causal questions. Int J Obes 32 (Suppl 3), S8–S14 (2008).

Download citation

  • Published:

  • Issue Date:

  • DOI:


This article is cited by


Quick links