DeepCausalMMM
=============

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       DeepCausalMMM
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       Advanced Marketing Mix Modeling with Causal Inference and Deep Learning
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**DeepCausalMMM** is a state-of-the-art Python package that combines deep learning with causal inference 
to understand the impact of marketing channels on business KPIs while learning causal relationships 
between channels through Directed Acyclic Graphs (DAGs).

.. code-block:: bash

   pip install deepcausalmmm
   # or latest from GitHub:
   pip install git+https://github.com/adityapt/deepcausalmmm.git

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       Get Started
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       API Docs
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**Key Features**
-------------------

* **Config-Driven**: Every setting configurable via config.py
* **GRU-Based Temporal Modeling**: Captures complex time-varying effects  
* **DAG Learning**: Discovers causal relationships between channels (upper-triangular mask by default, or opt-in **NOTEARS** continuous optimisation — see :doc:`tutorials/dag_notears`)
* **Multi-Region Support**: Handle geographic segmentation naturally
* **Robust Statistical Methods**: Huber loss, comprehensive metrics
* **Production Ready**: Battle-tested configurations and performance

**What Makes It Special**
----------------------------

* **Learnable Coefficient Bounds**: Channel-specific, data-driven constraints
* **Data-Driven Seasonality**: Automatic seasonal decomposition per region
* **Advanced Regularization**: L1/L2, sparsity, coefficient-specific penalties
* **14+ Interactive Visualizations**: Complete dashboard with business insights
* **Response Curves**: Non-linear saturation analysis with Hill equations
* **Budget Optimization**: Constrained optimization for optimal channel allocation
* **DMA-Level Contributions**: True economic impact calculation

**Perfect For**
------------------

* **Marketing Mix Modeling**: Understand true channel effectiveness
* **Attribution Analysis**: Discover incremental impact of each channel
* **Budget Optimization**: Data-driven media planning and allocation
* **Causal Discovery**: Learn how channels influence each other
* **Multi-Touch Attribution**: Beyond last-click attribution models

.. toctree::
   :maxdepth: 2
   :caption: Contents:

   installation
   quickstart
   tutorials/index
   api/index
   examples/index
   contributing

Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`