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    Data-driven causal discovery algorithms can assist life-course epidemiology by identifying plausible causal relationships, complementing expert-driven models. Combining both approaches strengthens causal model construction.

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    Area of Science:

    • Life-course epidemiology
    • Causal inference
    • Longitudinal data analysis

    Background:

    • Life-course epidemiology requires complex causal models to understand variable interactions over time.
    • Traditionally, these models are built from existing theories and prior research.

    Purpose of the Study:

    • To investigate if data-driven causal discovery algorithms can aid in constructing causal models for life-course epidemiology.
    • To compare models generated by data-driven algorithms with those developed by subject-field experts.

    Main Methods:

    • Utilized the longitudinal Metropolit Study dataset (Danish men, 1953-2017).
    • Constructed theory-driven models based on expert knowledge.
    • Generated data-driven models using the temporal Peter-Clark (TPC) algorithm, leveraging temporal information.

    Main Results:

    • The TPC algorithm identified some, but not all, causal relationships from the expert models.
    • Data-driven methods excelled at detecting direct causal links with high expert confidence.
    • A significant portion of novel causal relationships proposed by the data-driven model were deemed plausible upon review.

    Conclusions:

    • Data-driven methods can effectively support causal model development in life-course epidemiology.
    • The temporal Peter-Clark algorithm offers valuable insights, potentially uncovering overlooked causal hypotheses.
    • Integrating data-driven and theory-driven approaches can yield more robust and comprehensive causal models.