Causal Inference
This helps Claude understand and apply causal inference methods when you're trying to figure out if something actually caused an outcome rather than just correlating with it. You'd reach for this when analyzing A/B test results, estimating policy impacts from observational data, or dealing with confounders in your analysis. It covers the standard toolkit: propensity scores, instrumental variables, and heterogeneous treatment effects. Honestly, this is one of those areas where having the right statistical framework matters a lot, because it's easy to make confident but wrong claims about causation. The skill passed security audits and sits in a repository with 245 stars, so it's getting some traction with folks doing data analysis work.
- GitHub stars
- 317
- last updated
- Aug 21, 2026
Install
$ npx -y skills add aj-geddes/useful-ai-prompts --skill 'Causal Inference' --agent claude-codeInstalls into .claude/skills of the current project.
Files
Causal Inference
Overview
Causal inference determines cause-and-effect relationships and estimates treatment effects, going beyond correlation to understand what causes what.
When to Use
- Evaluating the impact of policy interventions or business decisions
- Estimating treatment effects when randomized experiments aren't feasible
- Controlling for confounding variables in observational data
- Determining if a marketing campaign or product change caused an outcome
- Analyzing heterogeneous treatment effects across different user segments
- Making causal claims from non-experimental data using propensity scores or instrumental variables
Key Concepts
- Treatment: Intervention or exposure
- Outcome: Result or consequence
- Confounding: Variables affecting both treatment and outcome
- Causal Graph: Visual representation of relationships
- Treatment Effect: Impact of intervention
- Selection Bias: Non-random treatment assignment
Causal Methods
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- Repository
- github.com/aj-geddes/useful-ai-prompts
- Category
- AI & Agent Building
- First seen
- Jun 3, 2026
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