# Analyze Causal Inference

> Estimate a causal effect with an explicit estimand, identification assumptions, design, diagnostics, and sensitivity. Use when the user asks whether a change caused an outcome, for difference-in-differences, IV, or target-trial emulation. Do not use to register a product experiment, forecast an unresolved future event, or critique a present explanation.

- Skill: `sylphxai/analyze-causal-inference` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add sylphxai/analyze-causal-inference`
- Raw SKILL.md: https://api.skillmd.com/api/skills/sylphxai/analyze-causal-inference/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: SylphxAI (https://skillmd.com/u/sylphxai)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/sylphxai/analyze-causal-inference

---


# Analyze Causal Inference

Name the estimand before choosing an estimator. If identification fails, say `not identified` instead of reporting a number. A narrow interval around a biased estimate is not strong causal evidence.

Open [causal inference methods](references/causal-inference-methods.md) when choosing an adjustment set, design, or sensitivity method.

Use `design-product-experiment` when assignment can still be designed. Use `forecast-with-calibration` for a future event. Use `analyze-system-dynamics` when the question is mechanism over time rather than one contrast.