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September 12, 2025Open Access

Models as Prediction Machines: How to Convert Confusing Coefficients into Clear Quantities

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Authors

JRJulia M. RohrerVAVincent Arel‐Bundock

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Overview

This approach clarifies causal and descriptive inference from regression models, showing diverse applications across studies.

Key Points

  • The alternative method simplifies the use of complex regression models for clearer data analysis and interpretation.
  • Notably, it allows researchers to explore causal effects and associations using various statistical designs including R and Python.
  • This process employs the marginaleffects package, making it versatile for multiple model types and research questions.
  • It highlights potential challenges like controlling for confounders and interpreting non-linear models effectively.

Cite This Study

Rohrer et al. (2025) studied this question.

synapsesocial.com/papers/68d44b3831b076d99fa54de5https://doi.org/10.31234/osf.io/g4s2a_v2
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Models as Prediction Machines: How to Convert Confusing Coefficients into Clear Quantities2025 · 3 citations
  2. 2Interpretable machine learning for psychological research: opportunities and pitfalls2025
  3. 3Reliable Estimation of Causal Effects Using Predictive Models2024 · 2 citations
  4. 4Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning2017 · 2,083 citations
  5. 5Trying to outrun causality with machine learning: Limitations of model explainability techniques for exploratory research.2024