Bayesian Rule Set: An Alternative to Qualitative Comparative Analysis

• In Event: Observational Causal Inference

Abstract

We introduce Bayesian Rule Set (BRS) as an alternative to Qualitative Comparative Analysis (QCA) when data are large and noisy. 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 in data. BRS has several advantages over QCA. First, BRS makes explicit trade-offs between in-sample fitness and model-complexity, thus avoiding overfitting and improving interpretability. Second, BRS is compatible with probabilistically generated outcomes and random noises 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 present BRS results. We illustrate BRS and these new tools using two empirical examples from sociology and political science.We introduce Bayesian Rule Set (BRS) as an alternative to Qualitative Comparative Analysis (QCA) when data are large and noisy. 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 in data. BRS has several advantages over QCA. First, BRS makes explicit trade-offs between in-sample fitness and model-complexity, thus avoiding overfitting and improving interpretability. Second, BRS is compatible with probabilistically generated outcomes and random noises 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 present BRS results. We illustrate BRS and these new tools using two empirical examples from sociology and political science.

Authors

• Albert Chiu, Stanford University

• Yiqing Xu, Stanford University