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Estimating causal parameters without target populations.

Eyal Shahar1

  • 1Division of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ 85724, USA. shahar@email.arizona.edu

Journal of Evaluation in Clinical Practice
|September 11, 2007
PubMed
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Causal inference in observational studies and trials should not be limited to a finite target population. New methods are proposed for estimating effects applicable to homogeneous, individual-level effects under indeterminism.

Area of Science:

  • Epidemiology
  • Causal Inference
  • Statistical Methodology

Background:

  • Methodologists increasingly advocate for causal effect estimates to apply to a finite target population.
  • Confounder adjustment methods often rely on the concept of a specified target population.

Purpose of the Study:

  • Critique the 'target population' paradigm in causal inference.
  • Examine the link between confounder adjustment methods and the target population concept.
  • Propose an alternative framework for causal inference.

Main Methods:

  • Literature review citing The American Journal of Epidemiology.
  • Analysis of causation models (determinism and stochastic causation).
  • Development of an alternative causal inference framework.

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Main Results:

  • The 'target population' epistemology is deemed scientifically irrelevant.
  • Standardization, inverse-probability-of-treatment weighting, and SMR-weighting are criticized.
  • A new framework is proposed where causal parameters are not population-specific.

Conclusions:

  • The target population approach in causal inference is fundamentally flawed.
  • Existing confounder adjustment methods based on this paradigm lack scientific relevance.
  • An alternative indeterministic model offers a more robust approach to estimating causal effects.