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name: performing-causal-analysis description: Fits causal models, estimates impacts, and plots results using CausalPy. Use when performing analysis with DiD, ITS, SC, or RD.

Performing Causal Analysis

Executes causal analysis using CausalPy experiment classes.

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