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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](reference/diff_in_diff.md)
* [Interrupted Time Series](reference/interrupted_time_series.md)
* [Synthetic Control](reference/synthetic_control.md)
