| 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)
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
