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causal-evidence-analyzer

Analyze causal relationships by evaluating scientific evidence across a research design hierarchy.

2|Updated May 31, 2026
Search Tags:#fact-checking#research#analysis#evidence-based#causality#scientific-method
One-click install
npx skills add https://github.com/kangning-huang/knhuang-research-skills --skill causal-evidence-analyzer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: causal-evidence-analyzer
Source: https://github.com/kangning-huang/knhuang-research-skills/tree/main/skills/causal-evidence-analyzer
Command: npx skills add https://github.com/kangning-huang/knhuang-research-skills --skill causal-evidence-analyzer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill addresses the common challenge of distinguishing between mere correlation and true causation in scientific and empirical claims, preventing the adoption of flawed conclusions.

Core Features & Use Cases

  • Evidence Hierarchy Classification: Automatically ranks evidence from Level 1 (Meta-analysis) to Level 5 (Expert opinion) to determine the strength of a claim.
  • Confounder Identification: Systematically flags potential third-factor variables, reverse causation, and selection bias that threaten causal validity.
  • Use Case: When evaluating a claim that a specific diet causes weight loss, this tool will search for relevant RCTs, identify potential confounders like exercise habits, and provide a calibrated conclusion based on the strength of the available literature.

Quick Start

Use the causal-evidence-analyzer to evaluate the claim that remote work causes a decrease in employee productivity.

Frequently Asked Questions about causal-evidence-analyzer

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

FAQPage Schema
How do I distinguish causation from correlation when analyzing research claims?▼

Systematically identify confounders in causal claims by using this analyzer to flag potential third-factor variables, reverse causation, and selection bias that threaten causal validity during evidence synthesis.

What is the best way to rank scientific evidence for fact-checking?▼

The best way to rank scientific evidence for fact-checking is classifying research designs from Level 1 meta-analyses down to Level 5 expert opinions to calibrate the strength of empirical claims.

Does this causal evidence analysis tool work for policy analysis scenarios?▼

Yes, this causal evidence analysis tool works for policy analysis by searching for and evaluating peer-reviewed studies and systematic reviews to synthesize calibrated conclusions for policy decisions.

Do I need web search capabilities to evaluate causal relationships?▼

Yes, you need web search capabilities to retrieve peer-reviewed studies and systematic reviews, because the analyzer relies on searching external scientific databases for evidence synthesis.

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