DeepCausalMMM

Spec sheet

Product

DeepCausalMMM is a Python package for marketing mix modeling that combines deep learning and causal inference to estimate marketing channel impacts on business KPIs.Read 4 Oct 2026 · deepcausalmmm.readthedocs.io

Intended users

The documentation identifies marketing mix modeling, attribution analysis, budget optimization, causal discovery, and multi-touch attribution as use cases.Read 4 Oct 2026 · deepcausalmmm.readthedocs.io

Support

The project README directs users to GitHub issues for bug reports and feature requests.Read 4 Oct 2026 · github.com

License

The project’s GitHub README states that it is released under the MIT License.Read 4 Oct 2026 · github.com

Analysis

It includes response curves for saturation analysis, constrained budget optimization, and DMA-level contribution calculations.Read 4 Oct 2026 · deepcausalmmm.readthedocs.io

Causal structure

It learns relationships between marketing channels with DAGs, using an upper-triangular mask by default and offering opt-in NOTEARS learning.Read 4 Oct 2026 · deepcausalmmm.readthedocs.io

Deployment limit

The installation page says a Docker image will be available soon.Read 4 Oct 2026 · deepcausalmmm.readthedocs.io

Geographic analysis

The package supports multi-region modeling and automatic seasonal decomposition per region.Read 4 Oct 2026 · deepcausalmmm.readthedocs.io

GPU support

The package automatically detects and uses CUDA when available; the documentation recommends a GPU for large models.Read 4 Oct 2026 · deepcausalmmm.readthedocs.io

Input data

The package expects NumPy arrays shaped by region, week, and channel for media and controls, plus a region-by-week target array.Read 4 Oct 2026 · deepcausalmmm.readthedocs.io

Installation

It can be installed from PyPI with pip or from the project’s GitHub repository.Read 4 Oct 2026 · deepcausalmmm.readthedocs.io

Requirements

The documented runtime requirements include Python 3.9+, PyTorch 2.0+, and NumPy 1.21 or later but below 2.0.Read 4 Oct 2026 · deepcausalmmm.readthedocs.io

Temporal modeling

Its GRU-based temporal model is designed to capture time-varying effects.Read 4 Oct 2026 · deepcausalmmm.readthedocs.io

Visualizations

The documentation describes 14+ interactive visualizations, including performance metrics, channel analysis, economic contributions, and DAG networks.Read 4 Oct 2026 · deepcausalmmm.readthedocs.io

Where Specifiction places it

• Marketing Analytics Software#45 of 506.6