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Randomized controlled trials aim for balanced groups, but this ideal often fails in practice. This study questions the necessity of perfect balance for valid causal inference in comparative studies.

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Area of Science:

  • Philosophy of Science
  • Biostatistics
  • Epidemiology

Background:

  • Randomized study group allocation is often presumed to create evenly balanced comparison groups for all confounding causes.
  • Philosophical arguments suggest that the balance assumption frequently fails in real-world randomized controlled trials.
  • The importance of this balance assumption as an ideal for causal inference remains debated.

Purpose of the Study:

  • To investigate whether perfect balance in comparison groups is a necessary condition for valid causal inference.
  • To explore the role of confounding variables and randomization in both ideal and real comparative group studies.
  • To propose a new framework for causal inference in comparative studies.

Main Methods:

  • A thought experiment, termed the CONFOUND study, was designed to address the research question.
  • The study involved two hypothetical scenarios: CONFOUND 1 and CONFOUND 2.
  • A new account of causal inference was developed based on the thought experiment's findings.

Main Results:

  • The balance assumption in randomized controlled trials is not always met in practice.
  • The study provides insights into the conditions required for causal inference even when perfect balance is absent.
  • Confounding variables can be understood as both causes and correlates, influencing causal interpretation.

Conclusions:

  • The ideal of perfect balance in randomized controlled trials may not be essential for drawing valid causal inferences.
  • A nuanced understanding of confounding variables and randomization is crucial for comparative group studies.
  • The proposed framework clarifies the mechanisms of causal inference in the presence of imperfectly balanced groups.