Performing Causal Analysis

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

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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

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