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.
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tessl review fix ./bundled/skills/performing-causal-analysis/SKILL.mdPerforming 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(), 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:
- Repository
- foryourhealth111-pixel/Vibe-Skills
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