Bayesian Rule Set: A Quantitative Alternative to Qualitative Comparative Analysis
41 Pages Posted:
Date Written: August 25, 2020
We introduce Bayesian Rule Set (BRS) as an alternative to Qualitative Comparative Analysis (QCA). BRS is an interpretable machine learning algorithm that classifies observations using rule sets, which are conditions connected by logical operators, e.g. IF (condition A AND condition B) OR (condition C), THEN Y~=~TRUE. Like QCA, BRS is highly interpretable and capable of revealing complex nonlinear relationships among explanatory and outcome variables. It has several advantages over QCA. First, as a machine learning algorithm, BRS makes explicit trade-offs between in-sample fitness and model-complexity, thus avoiding overfitting and improving interpretability. Second, BRS is more compatible with probabilistically generated outcomes and random errors that are prevalent with real-world data. Third, BRS is more computationally efficient with large datasets. We tailor BRS to social science settings, quantify its uncertainties, and develop new visualization tools to better present BRS results. Monte Carlo exercises show that BRS outperforms a state-of-the-art QCA algorithm when contradictory cases are present, with this advantage growing with data quantity. We illustrate BRS and new visualization tools using two empirical examples from sociology and political science.
Keywords: Bayesian Rule Set, Qualitative Comparative Analysis, interpretable machine learning
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