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Mendelian Randomization Methods for Causal Inference: Estimands, Identification and Inference.

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|January 22, 2026
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Summary
This summary is machine-generated.

Mendelian randomization (MR) is a powerful tool for inferring causal effects in health research. This review systematically covers MR methods, challenges like invalid instruments, and practical guidance for applied scientists.

Keywords:
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Area of Science:

  • Biomedical Research
  • Public Health
  • Genetic Epidemiology

Background:

  • Mendelian randomization (MR) utilizes genetic variants as instrumental variables to establish causal relationships between exposures and health outcomes.
  • MR offers a quasi-experimental approach to overcome confounding and reverse causation inherent in observational studies.
  • Despite its utility, MR faces methodological hurdles, including invalid or weak instruments and complex data structures.

Purpose of the Study:

  • To provide a systematic tutorial review of Mendelian randomization (MR) methods for causal inference.
  • To clarify causal interpretation, compare study designs, and offer practical guidance for researchers.
  • To cover challenges such as invalid instruments and recent advancements for omics data.

Main Methods:

  • Systematic overview of MR methodologies for causal inference.
  • Discussion of strategies for detecting and correcting invalid and weak instruments.
  • Integration of population-based vs. family-based and individual-level vs. summary-level data designs.

Main Results:

  • Comparison of one-sample vs. two-sample MR designs and their limitations.
  • Summary of recent methodological advances for complex scenarios (e.g., many weak instruments, omics data).
  • Illustrative applications using real-world data, including UK Biobank and Alzheimer's disease studies.

Conclusions:

  • This review serves as a tutorial reference for methodologists and applied scientists in causal inference.
  • It emphasizes well-defined causal questions and practical application of MR methods.
  • The content aims to enhance the rigorous application of MR in biomedical and public health research.