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This study examines outcome-dependent selection bias, extending previous work on collider and common cause biases. It investigates if such biases affect causal null hypothesis and allow for effect estimation.

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Area of Science:

  • Epidemiology
  • Causal Inference
  • Biostatistics

Background:

  • Selection bias is a critical issue in causal inference.
  • Previous work classified selection biases involving colliders and common causes.
  • Outcome-dependent selection presents unique challenges.

Purpose of the Study:

  • To extend the classification of selection biases to include outcome-dependent selection.
  • To assess the impact of outcome-dependent selection on the sharp causal null hypothesis.
  • To determine the feasibility of estimating causal effects in the presence of outcome-dependent selection.

Main Methods:

  • Theoretical analysis of selection mechanisms.
  • Discussion of implications for causal inference.
  • Extension of existing frameworks for selection bias.

Main Results:

  • Outcome-dependent selection, influenced directly by the outcome but not the exposure, is analyzed.
  • The study evaluates the preservation of the sharp causal null hypothesis under these conditions.
  • Conditions for estimating causal effects in selected and source populations are explored.

Conclusions:

  • Outcome-dependent selection requires careful consideration in causal analyses.
  • Understanding these biases is crucial for valid estimation of treatment effects.
  • This work provides a framework for addressing a novel class of selection biases.