Qualitative Comparative Analysis (QCA)

QCA codes comparable cases into a truth table and uses Boolean minimization to find which combinations of conditions are necessary or sufficient for an outcome. It is a real, published research method for medium-N comparative projects, and it does not fit a single-reasoner session: the input it needs (10 to 50 deeply-known comparable cases, calibrated on shared conditions) almost never exists, and at exactly the casual scale a session can muster, the simulation literature shows it certifies configurations from noise. This skill therefore does not build the truth table as if valid. It owns the request, leads with what the controlled-simulation evidence shows, and routes you to the evidence-based move your actual job needs. The output is an honest redirect brief, not a truth table with minimized configurations.

Before you run this: what the evidence shows

QCA is tier P (established research practice). It is a legitimate, peer-reviewed, software-supported method for genuine medium-N research, but there is no controlled study showing it improves an individual reasoner's judgment, and inside its own methods literature the core inferential claim is actively contested:

• Lucas and Szatrowski (2014, Sociological Methodology 44:1-79) ran QCA on simulated data with a known causal structure; across 70 solutions it recovered the correct causal story 3 times.
• Krogslund, Choi and Poertner (2015, Political Analysis 23:21-41) demonstrated parameter sensitivity in fsQCA, including random variables certified as "sufficient" - the method can manufacture confident causal-sounding output from noise.
• Baumgartner and Thiem (2020, Sociological Methods and Research 49:279-311) built inverse-search benchmarks and found the conservative and intermediate solution types drew false causal inferences.

The method fails three ways at session scale. The input (a real population of comparable, deeply-known cases) almost never exists for one reasoner. At exactly the accessible scale - a handful of past launches or deals, loosely coded - it manufactures false confidence, because that is the limited-diversity, casual-calibration regime the simulations condemn. And proper practice (calibration justification, robustness tests, negated-outcome analysis, within-case triangulation per Schneider and Wagemann 2010/2012) is a research program, not a session-sized move. So this skill will not hand you a truth table and a "sufficient configuration" verdict, because that artifact is exactly what the simulations condemn at the scale you can reach. It states the caveat and redirects.

When to Use

• The user asks for QCA (or a "truth table" / "configurational comparison" / "Boolean minimization" across cases) by name, and an honest warning plus a redirect serves them better than silently building the unreliable artifact or refusing outright.

When NOT to Use