causal_analysis

What problem does it solve?

This skill identifies and evaluates causal relationships within a given dataset or body of evidence, moving beyond simple correlations by applying established causal inference frameworks (e.g., Bradford Hill criteria, counterfactual reasoning) to assess whether one variable (the cause) is responsible for changes in another (the effect).

Core Features & Use Cases

• Identify candidate cause-effect relationships in the provided context.
• Classify evidence into a strict hierarchy of study types (RCT, observational, mechanistic, expert_opinion) and evaluate its methodological rigor.
• Document confounding variables, reverse causality, selection bias, or other threats to internal validity.
• Produce a structured JSON output that clearly separates robust causal conclusions from mere associations and highlights critical gaps in evidence.

Quick Start

Analyze a dataset or narrative text to extract causal claims and produce a JSON report.