Optimizing Consistency and Coverage in Configurational Causal Modeling

Michael Baumgartner,Mathias Ambühl

doi:10.1177/0049124121995554

Michael Baumgartner, Mathias Ambühl

Open Access

https://doi.org/10.1177/0049124121995554

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Abstract

Consistency and coverage are two core parameters of model fit used by configurational comparative methods (CCMs) of causal inference. Among causal models that perform equally well in other respects (e.g., robustness or compliance with background theories), those with higher consistency and coverage are typically considered preferable. Finding the optimally obtainable consistency and coverage scores for data [Formula: see text], so far, is a matter of repeatedly applying CCMs to [Formula: see text] while varying threshold settings. This article introduces a procedure called ConCovOpt that calculates, prior to actual CCM analyses, the consistency and coverage scores that can optimally be obtained by models inferred from [Formula: see text]. Moreover, we show how models reaching optimal scores can be methodically built in case of crisp-set and multi-value data. ConCovOpt is a tool, not for blindly maximizing model fit, but for rendering transparent the space of viable models at optimal fit scores in order to facilitate informed model selection—which, as we demonstrate by various data examples, may have substantive modeling implications.

Highlights

Over the past three decades, different variants of configurational comparative methods (CCMs) have gradually been added to the tool kit for causal data analysis in many disciplines, ranging from social and political science to business administration, evaluation science, and on to public health and psychology
Coincidence Analysis (CNA) and CCubes, we introduce a procedure for building CCM models realizing any con-cov optimum for cs and mv data
We end this article by putting ConCovOpt into proper methodological perspective

Summary

Introduction

Over the past three decades, different variants of configurational comparative methods (CCMs) have gradually been added to the tool kit for causal data analysis in many disciplines, ranging from social and political science to business administration, evaluation science, and on to public health and psychology. Rather, optimizing consistency and coverage is a matter of repeatedly running QCA and CNA on the data while varying relevant thresholds and comparing the fit scores of resulting models.

Results

Conclusion