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1 results

  • 13 - Weighting the Multiverse

  • from Part III - Expanding the Multiverse
  • Cristobal Young, Cornell University, New York, Erin Cumberworth, Cornell University, New York
  • Book:
    Multiverse Analysis
    Published online:
    28 February 2025
    Print publication:
    06 March 2025, pp 208-225

  • Summary

    Are some models better than others? Yes. But can we weight models by the probability that they are true? That is harder than it sounds. In this chapter we cover various methods for weighting the models in a multiverse and assess their strengths and weaknesses using a dataset on how air pollution near schools can affect student learning. Weighting models creates a tension between model selection and model robustness, and authors must be clear about how model weights change the distribution of results. We recommend uniform weights as a transparent default, and if further weighting is desired, either double lasso or influence weighting appears best for inference.