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