Performing causal analysis

Skill foryourhealth111-pixel/Vibe-Skills/bundled/skills/performing-causal-analysis

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.From its SKILL.md

Install

npx -y skills add foryourhealth111-pixel/Vibe-Skills --skill performing-causal-analysis

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SKILL.md

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Performing Causal Analysis

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(), and plot().

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:

What ships with it: 3 files

4.6 KB alongside SKILL.md