DeepCausalMMM
C
C tier on Marketing Analytics SoftwareScore 5.9 · #18 of 49
- Android app
- Not listed
- Free plan
- No
Summary
DeepCausalMMM is ranked #18 of 49 in marketing analytics software on Everything Xiaomi.
Compared on marketing analytics software
- Spend tracking
- Yesdeepcausalmmm.readthedocs.io
- Custom dashboards
- Yesdeepcausalmmm.readthedocs.io
Facts
- 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.deepcausalmmm.readthedocs.io · 4 Oct 2026
- Temporal modeling
- Its GRU-based temporal model is designed to capture time-varying effects.deepcausalmmm.readthedocs.io · 4 Oct 2026
- Causal structure
- It learns relationships between marketing channels with DAGs, using an upper-triangular mask by default and offering opt-in NOTEARS learning.deepcausalmmm.readthedocs.io · 4 Oct 2026
- Geographic analysis
- The package supports multi-region modeling and automatic seasonal decomposition per region.deepcausalmmm.readthedocs.io · 4 Oct 2026
- Analysis
- It includes response curves for saturation analysis, constrained budget optimization, and DMA-level contribution calculations.deepcausalmmm.readthedocs.io · 4 Oct 2026
- Visualizations
- The documentation describes 14+ interactive visualizations, including performance metrics, channel analysis, economic contributions, and DAG networks.deepcausalmmm.readthedocs.io · 4 Oct 2026
- Input data
- The package expects NumPy arrays shaped by region, week, and channel for media and controls, plus a region-by-week target array.deepcausalmmm.readthedocs.io · 4 Oct 2026
- Installation
- It can be installed from PyPI with pip or from the project’s GitHub repository.deepcausalmmm.readthedocs.io · 4 Oct 2026
- Requirements
- The documented runtime requirements include Python 3.9+, PyTorch 2.0+, and NumPy 1.21 or later but below 2.0.deepcausalmmm.readthedocs.io · 4 Oct 2026
- GPU support
- The package automatically detects and uses CUDA when available; the documentation recommends a GPU for large models.deepcausalmmm.readthedocs.io · 4 Oct 2026
- Deployment limit
- The installation page says a Docker image will be available soon.deepcausalmmm.readthedocs.io · 4 Oct 2026
- License
- The project’s GitHub README states that it is released under the MIT License.github.com · 4 Oct 2026
- Support
- The project README directs users to GitHub issues for bug reports and feature requests.github.com · 4 Oct 2026
- Intended users
- The documentation identifies marketing mix modeling, attribution analysis, budget optimization, causal discovery, and multi-touch attribution as use cases.deepcausalmmm.readthedocs.io · 4 Oct 2026
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Sources
- deepcausalmmm.readthedocs.io/en/latest/· checked 4 Oct 2026
- deepcausalmmm.readthedocs.io/en/latest/quickstart.html· checked 4 Oct 2026
- deepcausalmmm.readthedocs.io/en/latest/installation.html· checked 4 Oct 2026
- github.com/adityapt/deepcausalmmm· checked 4 Oct 2026