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Updated: Jun 12, 2025

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
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What randomization can and cannot guarantee.

Peng Ding1

  • 1Statistics University of California, Berkeley.

Observational Studies
|June 9, 2025
PubMed
Summary
This summary is machine-generated.

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Randomized controlled trials (RCTs) offer stronger statistical inference than observational studies, contrary to claims that RCTs are not the gold standard. This highlights the value and limitations of randomization in causal inference.

Area of Science:

  • Statistics
  • Causal Inference
  • Epidemiology

Background:

  • The value of randomized controlled trials (RCTs) in causal inference is debated.
  • Robins and Ritov (1997) established key results regarding statistical inference in RCTs versus observational studies.
  • Pearl and Mackenzie (2018) controversially claimed RCTs are not the gold standard for causal analysis.

Purpose of the Study:

  • To clarify the statistical inference advantages of RCTs over unconfounded observational studies.
  • To address and refute the claim that RCTs are not the gold standard in causal analysis.
  • To provide a broader review of randomization's guarantees and limitations in causal inference.

Main Methods:

  • Review and delineation of key results from Robins and Ritov (1997).
Keywords:
causal inferenceoverlappotential outcomepropensity scorerandomization testrandomized controlled trial

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  • Comparative analysis of statistical inference in randomized controlled trials versus unconfounded observational studies.
  • Discussion of randomization-based inference and its limitations.

    • Main Results:

    • Randomized controlled trials (RCTs) provide significantly stronger statistical inference than unconfounded observational studies.
    • Nonparametric identification of causal effects is identical in both RCTs and observational studies, yet inference differs.
    • Claims that RCTs are not the gold standard are misleading for finite sample causal effect estimation.

    Conclusions:

    • Randomization in RCTs is crucial for robust causal effect estimation, especially with finite sample sizes.
    • While randomization is valuable, it has limitations for complex causal inference questions.
    • Sensitivity analysis remains important for strengthening causal claims from observational studies.