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

• URL: http://arxiv.org/abs/2510.13087v2
• Date: Thu, 23 Oct 2025 02:49:44 GMT
• Title: DeepCausalMMM: A Deep Learning Framework for Marketing Mix Modeling with Causal Inference
• Authors: Aditya Puttaparthi Tirumala,
• Abstract summary: DeepCausalMMM is a Python package that addresses limitations by combining deep learning, causal inference, and advanced marketing science.<n>The package uses Gated Recurrent Units (GRUs) to automatically learn temporal patterns such as adstock (carryover effects) and lag.<n>It also implements Hill equation-based saturation curves to model diminishing returns and optimize budget allocation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Marketing Mix Modeling (MMM) is a statistical technique used to estimate the impact of marketing activities on business outcomes such as sales, revenue, or customer visits. Traditional MMM approaches often rely on linear regression or Bayesian hierarchical models that assume independence between marketing channels and struggle to capture complex temporal dynamics and non-linear saturation effects [@Chan2017; @Hanssens2005; @Ng2021Bayesian]. **DeepCausalMMM** is a Python package that addresses these limitations by combining deep learning, causal inference, and advanced marketing science. The package uses Gated Recurrent Units (GRUs) to automatically learn temporal patterns such as adstock (carryover effects) and lag, while simultaneously learning statistical dependencies and potential causal structures between marketing channels through Directed Acyclic Graph (DAG) learning [@Zheng2018NOTEARS; @Gong2024CausalMMM]. Additionally, it implements Hill equation-based saturation curves to model diminishing returns and optimize budget allocation. Key features include: (1) a data-driven design where hyperparameters and transformations (e.g., adstock decay, saturation curves) are learned or estimated from data with sensible defaults, rather than requiring fixed heuristics or manual specification, (2) multi-region modeling with both shared and region-specific parameters, (3) robust statistical methods including Huber loss and advanced regularization, (4) comprehensive response curve analysis for understanding channel saturation.

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