confounding-assessment
CommunityIdentify and analyze confounding factors.
Education & Research#causal inference#epidemiology#DAG#bias assessment#confounding#observational studies
Authorj-walheim
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill systematically identifies potential confounding variables in observational studies, assesses their measurability, and helps construct causal diagrams to understand their impact on the exposure-outcome relationship.
Core Features & Use Cases
- Confounder Enumeration: Lists the most significant potential confounders for a given exposure-outcome pair.
- Causal DAG Construction: Generates a visual representation of causal relationships.
- Collider Detection: Identifies potential issues with collider bias in adjustment strategies.
- Use Case: When evaluating a new drug's effectiveness based on observational data, this Skill helps researchers identify and account for factors like patient demographics, lifestyle choices, or pre-existing conditions that might distort the true effect of the drug.
Quick Start
Use the confounding-assessment skill to enumerate confounders for the exposure 'drug_a' and outcome 'heart_attack'.
Dependency Matrix
Required Modules
networkxmatplotlibpandaspydot
Components
scriptsreferences
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: confounding-assessment Download link: https://github.com/j-walheim/Critical-AI-Scientist/archive/main.zip#confounding-assessment Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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