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qca

Calibrates conditions and computes necessity/sufficiency relationships for comparative case analysis in Python workflows.

Updated Aug 27, 2026
Search Tags:#set theory#calibration#causal inference#truth table#qca#Boolean minimization#small-n analysis
One-click install
npx skills add https://github.com/Yuuqq/claude-social-science-skills --skill qca-yuuqq
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: qca
Source: https://github.com/Yuuqq/claude-social-science-skills/tree/main/social-science-skills/qca
Command: npx skills add https://github.com/Yuuqq/claude-social-science-skills --skill qca-yuuqq

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and assets (resource) and references (resource) components.

What problem does it solve?

QCA solves the problem of identifying which combinations of conditions (not single variables) are associated with an outcome when your study has small-to-medium numbers of cases and causal complexity such as equifinality and asymmetry.

Core Features & Use Cases

  • Calibration: Convert raw measurements into crisp-set (csQCA) or fuzzy-set (fsQCA) membership scores so conditions can be treated as sets.
  • Truth table construction: Summarize which condition configurations empirically coincide with the outcome using frequency and consistency thresholds.
  • Necessity and sufficiency analysis: Test whether conditions are necessary and use Boolean minimization to derive sufficiency solutions (configurational explanations).
  • Workflow support: Includes an end-to-end Python research workflow and robustness-check script for sensitivity analysis across thresholds and case inclusion.

Use cases include: analyzing democratic transitions across a limited set of countries, evaluating policy outcomes from case-based evidence, and generating interpretable causal configurations when multiple paths can lead to the same result.

Quick Start

Use the QCA skill to analyze a small set of calibrated cases by building the truth table, running necessity analysis, minimizing sufficiency configurations, and reporting consistency and coverage for each derived path.

Frequently Asked Questions about qca

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I identify causal pathways in small-N analysis when multiple conditions lead to the same outcome?▼

Qualitative comparative analysis identifies causal pathways in small-N analysis by calibrating conditions into set membership, constructing a truth table, and applying Boolean minimization to derive multiple configurational explanations for equifinality. It computes consistency and coverage for each path.

What is the difference between crisp-set and fuzzy-set calibration for truth table construction?▼

Crisp-set calibration assigns binary membership scores, while fuzzy-set calibration assigns continuous values between zero and one. Both convert raw measurements into set membership scores for truth table construction, enabling the analysis of necessity and sufficiency relationships across cases.

How do I test necessity and sufficiency relationships for an outcome using set theory?▼

To test necessity and sufficiency relationships, you construct a truth table from calibrated data and calculate set-theoretic consistency and coverage. Consistency thresholds determine if a condition is necessary, while Boolean minimization derives sufficient configurational paths.

Can I use QCA for temporal sequencing tasks and asymmetric causation across medium-N cases?▼

Yes, QCA supports temporal sequencing tasks and asymmetric causation across small-to-medium-N cases. It handles equifinality by computing multiple conjunctural configurations that produce the same outcome, using set-theoretic consistency and coverage calculations.

How do I run robustness checks and sensitivity analysis for QCA threshold selections?▼

You run robustness checks using a dedicated sensitivity analysis script to evaluate stability across different frequency and consistency thresholds. This workflow verifies that derived configurational paths remain reliable when case inclusion criteria and thresholds vary.

When should I not use QCA for evaluating case-based policy outcomes?▼

You should not use QCA when your study has large numbers of cases or lacks causal complexity. QCA is designed for small-to-medium-N analysis where equifinality and asymmetry exist, requiring calibrated data inputs to produce interpretable configuration paths.

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