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causal

Estimate treatment effects from observational data with assumption checks and sensitivity analysis.

16|7|Updated Apr 1, 2026
Search Tags:#reporting#assumption-checking#causal-inference#sensitivity-analysis#observational-data#treatment-effects#causal-workflow
One-click install
npx skills add https://github.com/ai-analyst-lab/ai-analyst-plus --skill causal-ai-analyst-lab
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: causal
Source: https://github.com/ai-analyst-lab/ai-analyst-plus/tree/main/.claude/skills/causal
Command: npx skills add https://github.com/ai-analyst-lab/ai-analyst-plus --skill causal-ai-analyst-lab

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Estimate treatment effects from observational data when experiments aren't feasible, providing explicit assumption checks, sensitivity analyses, and mandatory caveats to ensure credible conclusions.

Core Features & Use Cases

  • End-to-end causal workflow: select methods, analyze data, check assumptions, assess sensitivity, and generate reports.
  • Mode-based execution: supports /causal select, /causal analyze, /causal check, /causal sensitivity, /causal report, and /causal full.
  • Real-world use: evaluate feature impacts or policy changes when randomization is unavailable, with transparent limitations and guidance.

Quick Start

Invoke the causal inference workflow by calling the /causal [mode] command in Claude Code to begin the analysis on your dataset.

Frequently Asked Questions about causal

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

FAQPage Schema
How do I estimate treatment effects from observational data when randomization is not possible?▼

Estimating treatment effects from observational data requires a causal inference workflow that applies matching or regression methods, runs assumption checks, and performs sensitivity analysis to ensure credible conclusions without randomization.

What is the best way to check assumptions and sensitivity in a causal inference workflow?▼

Checking assumptions and sensitivity in causal inference involves dedicated validation steps that test underlying statistical assumptions and assess how robust treatment effect estimates remain against unobserved confounding variables.

Can I run a full causal analysis end-to-end without writing separate scripts for each step?▼

Yes, running a full end-to-end causal analysis is possible through a single comprehensive mode that automatically chains method selection, data analysis, assumption checks, sensitivity analysis, and stakeholder reporting together.

Does causal inference work for evaluating feature impacts and policy changes in real-world datasets?▼

Causal inference works for evaluating feature impacts and policy changes in real-world datasets by applying observational data methods that provide transparent limitations and actionable, caveated outputs for stakeholders.

What are the limitations of using observational data for treatment effect estimation?▼

The primary limitation of using observational data for treatment effect estimation is unmeasured confounding, which necessitates explicit assumption checks, sensitivity analysis, and mandatory caveats to maintain credible conclusions.

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