Proponents of set-theoretic comparative methods (STCM) sharply differentiate their approach from quantitative analysis—unlike many researchers who focus on integrating qualitative and quantitative methods. This article engages these opposing views by demonstrating shared foundations between STCM and quantitative techniques. First, it shows how the quantitative practice of analyzing cases that exhibit variation on both the explanatory conditions and the outcome—for example, all four cells of a 2 × 2 table—guards against misleading conclusions about necessary/sufficient conditions. Hence, conventional statistical ideas about association are relevant for STCM. Second, STCM’s tools for analyzing causal complexity share important features with regression interaction terms. Third, scrutinizing these shared foundations suggests how stronger theoretical and empirical standards for causal inference with deterministic hypotheses can be established. Focusing on shared foundations and recognizing that STCM does not genuinely break new inferential ground facilitate new opportunities for strengthening comparative research tools, rather than unproductively overemphasizing differences from mainstream methods.
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