name: performing-causal-analysis description: 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.

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

1. Load Data: Ensure data is in a Pandas DataFrame.
2. Initialize Experiment: Use the appropriate class (see References).
3. Fit & Model: Models are fitted automatically upon initialization if arguments are provided.
4. 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:

• Difference-in-Differences
• Interrupted Time Series
• Synthetic Control