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1 results

  • 6 - Influence Analysis and Scope Conditions

  • from Part II - The Computational 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 70-97

  • Summary

    Why do different models give different results? Which modeling assumptions matter most? These are questions of model influence. Standard regression results fail to address simple questions like, which control variables are important for getting this result? In this chapter we lay out a framework for thinking about influence and draw on empirical examples to illustrate. When a result is not fully robust, the influence analysis provides methodological explanations for the failure of robustness. These explanations can be considered methodological scope conditions – they explain why a hypothesis can be supported in some cases but not in others. We also show how multiverse results can help inform the method of sensitivity analysis