alterlab-qca

name	alterlab-qca
description	Runs Qualitative Comparative Analysis — crisp-set (csQCA), multi-value (mvQCA), and fuzzy-set (fsQCA) — for small-to-medium-N configurational research: calibrating raw data into set membership, building and refining a truth table, and Boolean minimization into conservative / parsimonious / intermediate solutions with consistency and coverage. Because there is no maintained Python QCA library, it shells out to R's QCA package (calibrate, truthTable, minimize) via Rscript and documents that dependency honestly rather than faking a Python API. Use when the request mentions QCA, fsQCA, csQCA, configurational or set-theoretic analysis, necessary/sufficient conditions, truth tables, or calibration of conditions. For net-effect estimation of a single treatment prefer alterlab-causal-inference; for interpretive analysis prefer alterlab-qualitative-methods. Part of the AlterLab Academic Skills suite.
license	MIT
allowed-tools	Read Bash(Rscript:*)
compatibility	Requires R with the QCA package (install.packages('QCA'); optionally SetMethods). NO maintained Python QCA library exists, so this skill shells to R via Rscript — declare R as a dependency. No API key. On the Anthropic API (no runtime install) document the R requirement and degrade gracefully.
metadata	{"skill-author":"AlterLab","version":"1.0.0","depends_on":"alterlab-ssci-design-gate (configurational vs net-effects), alterlab-qualitative-methods; audited by alterlab-ssci-inference-gate"}

QCA — Set-Theoretic, Configurational, Equifinal

Skill type: ANALYSIS MODULE. Qualitative Comparative Analysis (Ragin) treats cases as configurations of conditions and asks which combinations are consistently sufficient (or necessary) for an outcome. It is not net-effects regression: it assumes equifinality (several paths to the same outcome), conjunctural causation (conditions work in combination), and asymmetry (the path to Y is not the mirror of the path to not-Y).

Core Mission

QCA IS ABOUT COMBINATIONS AND SET RELATIONS, NOT AVERAGE MARGINAL EFFECTS. CALIBRATE, THEN MINIMIZE.

When to Use This Skill

• "Run a fuzzy-set QCA / fsQCA on my configurational data."
• "Which combinations of conditions are sufficient for the outcome?"
• "Calibrate these variables into set membership and build a truth table."
• "Test necessary and sufficient conditions across my ~20 cases."

Does NOT Trigger

The request is really about…	Route to	Why not this skill
The net effect of one treatment (DiD/IV/RDD/regression)	alterlab-causal-inference	Correlational/marginal effects, not set relations.
Whether a configurational design is even appropriate	alterlab-ssci-design-gate	Design routing, upstream.
Interpretive/thematic analysis of qualitative data	alterlab-qualitative-methods	Not set-theoretic.
Plain descriptive statistics	alterlab-statistical-analysis	No calibration/minimization.

The R path (verified — QCA package, no Python equivalent)

There is no maintained Python QCA library (the only PyPI option, scpQCA, is stale and non-standard). The field standard is Adrian Dușa's R QCA package. Shell to it via Rscript. The three verified calls:

library(QCA)

# 1. Calibrate raw columns into fuzzy (or crisp) set membership.
#    thresholds for the direct method = c(exclusion, crossover, inclusion), ascending.
dfA  calibraterawA type   thresholds    



tt  truthTabledf outcome   conditions  
                 incl.cut   n.cut   show.cases   sort.by  



sol_c  minimizett details                         
sol_p  minimizett include   details          
sol_i  minimizett include   dir.exp  ABC details