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6 - Influence Analysis and Scope Conditions

from Part II - The Computational Multiverse

Published online by Cambridge University Press:  28 February 2025

Cristobal Young  and
Erin Cumberworth
Cristobal Young
Affiliation:
Cornell University, New York
Erin Cumberworth
Affiliation:
Cornell University, New York
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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

Keywords

influence analysisinfluence effectsinfluence regressionmeta-regressionCook’s Ddiscrimination in mortgage lendingjob training effectivenesssuppression effectsreplicationsensitivity analysis
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Chapter
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Multiverse Analysis
Computational Methods for Robust Results
 , pp. 70 - 97
Publisher: Cambridge University Press
Print publication year: 2025

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