Performing Causal Analysis
SkillMediaLets your agent estimate causal effects from existing data using methods like DiD, synthetic control, and regression discontinuity.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Performing Causal Analysis skill
About this capability
Estimate causal effects from existing data. Use when fitting or interpreting DiD, ITS, synthetic control, regression discontinuity, or other treatment-effect analyses, including robustness checks and counterfactual plots. For choosing a study design before analysis, use designing-experiments instead
What this skill tells your AI
The instructions your AI receives, as published by foryourhealth111-pixel/vibe-skills in bundled/skills/performing-causal-analysis/SKILL.md and read by ahel’s review.
Executes causal analysis on existing data. This skill owns model setup, treatment-effect estimation, counterfactual comparison, robustness checks, and interpretation of fitted causal results.
It does not own the earlier question of which experiment or quasi-experiment should be designed before analysis begins.
Workflow
- Load Data: Ensure data is in a Pandas DataFrame.
- Initialize Experiment: Use the appropriate class (see References).
- Fit & Model: Models are fitted automatically upon initialization if arguments are provided.
- Analyze Results: Use
summary(),print_coefficients(), andplot().
Core Methods
experiment.summary(): Prints model summary and main results.experiment.plot(): Visualizes observed vs. counterfactual.experiment.print_coefficients(): Shows model coefficients.
References
Detailed usage for specific methods:
Signals
- GitHub stars
- 3k
- Forks
- 277
- Last commit
- Aug 2026
Advanced
- Catalog kind
- skill
- Gateway key
performing-causal-analysis- Source
- github.com/foryourhealth111-pixel/vibe-skills