Examples
========

The ``examples`` directory contains runnable Jupyter notebooks demonstrating **mini-causal** features. These notebooks provide practical, hands-on examples of how to use the library for various causal analysis tasks.

Available Notebooks
------------------

Causal Models (Classifier)
~~~~~~~~~~~~~~~~~~~~~~~~~

**File:** ``examples/causal_models_classifier_example.ipynb``

This notebook demonstrates how to compare two classification models using the ``causality`` module. Learn how to:

- Initialize and configure ``CausalModelsClassifier``
- Run causal comparison between models
- Interpret the results
- Visualize model differences

Key concepts covered:

- Causal metrics for model comparison
- Statistical tests for causal effects
- Practical workflow for model evaluation

Causal Counterfactual
~~~~~~~~~~~~~~~~~~~~

**File:** ``examples/mini_causal_causal_counterfactual_example.ipynb``

This notebook demonstrates the complete counterfactual workflow using the ``causal_counter`` module. Learn how to:

- Set up counterfactual analysis
- Generate counterfactual explanations
- Evaluate counterfactual quality
- Interpret the results

Key concepts covered:

- Counterfactual generation algorithms
- Feature importance through counterfactuals
- Model interpretability

Partial Counterfactual (Prostate Dataset)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

**File:** ``examples/mini_causal_prostate_with_partial_counterfactual.ipynb``

This notebook demonstrates targeted counterfactual analysis on the prostate dataset using the ``partial_counter`` module. Learn how to:

- Focus on specific feature subsets
- Run partial counterfactual analysis
- Compare results with full counterfactuals
- Interpret targeted interventions

Key concepts covered:

- Partial counterfactual generation
- Feature subset selection
- Targeted causal analysis
- Domain-specific application

Running the Examples
------------------

Prerequisites
~~~~~~~~~~~~

Ensure you have Jupyter installed:

.. code-block:: bash

   pip install jupyter

Optional packages for visualization:

.. code-block:: bash

   pip install matplotlib seaborn

Launching Jupyter
~~~~~~~~~~~~~~~~

1. Navigate to the repository root:

   .. code-block:: bash

      cd mini-causal

2. Start Jupyter Lab or Notebook:

   .. code-block:: bash

      jupyter lab
      # or
      jupyter notebook

3. Open the notebooks from the ``examples`` directory

Tips for Working with Examples
-----------------------------

- Start with the classifier example if you're new to causal analysis
- Each notebook includes detailed explanations and code comments
- Modify the code to use your own datasets and models
- Refer to the module documentation for API details

Note: Some examples may require additional datasets. These are typically included in the ``datasets`` directory or will be downloaded automatically.