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Estimating the potential impacts of intervention from observational data: methods for estimating causal attributable risk in a cross-sectional analysis of depressive symptoms in Latin America
  1. Nancy L Fleischer1,
  2. Lia C H Fernald2,
  3. Alan E Hubbard2
  1. 1 University of Michigan, United States;
  2. 2 UC Berkeley School of Public Health, United States
  1. * Corresponding author; email: nancyfl{at}


Background: The field of epidemiology struggles both with enhancing causal inference in observational studies and providing useful information for policy makers and public health workers focusing on interventions. Population intervention models, analogous to population attributable fractions, estimate the causal impact of interventions in a population, and are one option for understanding the relative importance of various risk factors. With population intervention parameters, risk factors are effectively standardized, allowing one to compare their values directly and determine which potential intervention may have the greatest impact on the outcome.

Methods: In this paper we examine the difference between total effects and population intervention parameters, using naïve, G-computation and inverse probability of treatment weighting approaches. We explore the differences between these parameters and the intuitions they provide using data from a 2003 cross-sectional study in rural Mexico.

Results: We discuss the assumptions, specific analytic steps, limitations and interpretations of the total effects and population intervention parameters, and provide code in Stata.

Conclusion: Population intervention parameters are a valuable and straight-forward approach in epidemiologic studies for making causal inference from the data while also supplying information that is relevant for researchers, public health practitioners, and policy makers.

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