# DeepCausalMMM: A Deep Learning Framework for Marketing Mix Modeling with Causal Structure Learning

arXiv: https://arxiv.org/abs/2510.13087 · HTML: https://mlsanity.com/paper/2510.13087/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 11 references that are in the map.
- First posted on arXiv 15 Oct 2025.

## Builds on (most cited first, 5 of 5)

- [DAGs with NO TEARS: Continuous Optimization for Structure Learning](https://mlsanity.com/paper/1803.01422/) (2018)
- [A Survey of Deep Causal Models and Their Industrial Applications](https://mlsanity.com/paper/2209.08860/) (2022)
- [CausalMMM: Learning Causal Structure for Marketing Mix Modeling](https://mlsanity.com/paper/2406.16728/) (2024)
- [Bayesian Time Varying Coefficient Model with Applications to Marketing Mix Modeling](https://mlsanity.com/paper/2106.03322/) (2021)
- [Packaging Up Media Mix Modeling: An Introduction to Robyn's Open-Source Approach](https://mlsanity.com/paper/2403.14674/) (2024)

## Abstract (arXiv)

Marketing Mix Modeling (MMM) estimates the impact of marketing activities on business outcomes such as sales or revenue. Traditional MMM approaches rely on linear regression or Bayesian hierarchical models that assume channel independence and struggle to capture temporal dynamics and non-linear saturation. DeepCausalMMM addresses these limitations by combining deep learning, causal inference, and marketing science. It uses Gated Recurrent Units (GRUs) to learn temporal patterns (adstock, lag) while learning statistical dependencies between channels through Directed Acyclic Graph (DAG) structure with upper triangular constraints. It implements Hill equation saturation curves for diminishing returns and budget optimization. Key features: (1) data-driven hyperparameters learned from data with defaults, (2) linear mean scaling of the dependent variable, (3) configurable attribution priors with dynamic loss scaling, (4) multi-region modeling with shared and region-specific parameters, (5) robust methods including Huber loss, (6) response curve analysis.