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Get Started Free →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.
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 17% | 0% |
| case-02 | ✗→✓ | ▲ Improved | -50% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 25% | 0% |
| case-05 | ✗→✓ | ▲ Improved | -53% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -76% | 0% |
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.
summary(), print_coefficients(), and plot().experiment.summary(): Prints model summary and main results.experiment.plot(): Visualizes observed vs. counterfactual.experiment.print_coefficients(): Shows model coefficients.Detailed usage for specific methods:
Other measured skills in the registry, with their headline benchmark lift.