In online advertising, marketing mix modeling (MMM) is employed to predict the gross merchandise volume (GMV) of brand shops and help decision-makers to adjust the budget allocation of various advertising channels. Traditional MMM methods leveraging regression techniques can fail in handling the complexity of marketing. Although some efforts try to encode the causal structures for better prediction, they have the strict restriction that causal structures are prior-known and unchangeable. In this paper, we define a new causal MMM problem that automatically discovers the interpretable causal structures from data and yields better GMV predictions. To achieve causal MMM, two essential challenges should be addressed: (1) Causal Heterogeneity. The causal structures of different kinds of shops vary a lot. (2) Marketing Response Patterns. Various marketing response patterns i.e., carryover effect and shape effect, have been validated in practice. We argue that causal MMM needs dynamically discover specific causal structures for different shops and the predictions should comply with the prior known marketing response patterns. Thus, we propose CausalMMM that integrates Granger causality in a variational inference framework to measure the causal relationships between different channels and predict the GMV with the regularization of both temporal and saturation marketing response patterns. Extensive experiments show that CausalMMM can not only achieve superior performance of causal structure learning on synthetic datasets with improvements of 5.7%\sim 7.1%, but also enhance the GMV prediction results on a representative E-commerce platform.

翻译：在线广告领域中，营销组合建模（MMM）被用于预测品牌店铺的商品交易总额（GMV），并协助决策者调整各广告渠道的预算分配。传统基于回归技术的MMM方法难以处理营销活动的复杂性。尽管已有研究尝试通过编码因果结构以提升预测性能，但这些方法严格限定因果结构需预先已知且不可变更。本文定义了一种新型因果式MMM问题，其能够从数据中自动发现可解释的因果结构，并获得更优的GMV预测结果。为实现因果式MMM，需解决两个核心挑战：（1）因果异质性。不同类型店铺的因果结构差异显著。（2）营销响应模式。实践中已验证多种营销响应模式（如延迟效应与形态效应）的存在。我们认为因果式MMM需为不同店铺动态发现特定因果结构，且预测结果应符合已知的营销响应模式。为此，本文提出CausalMMM模型，该模型将格兰杰因果融入变分推断框架，以度量不同渠道间的因果关系，并通过时间维度与饱和态营销响应模式的双重正则化约束进行GMV预测。大量实验表明，CausalMMM不仅在合成数据集上实现了因果结构学习的性能提升（5.7%～7.1%），同时也在典型电商平台数据上显著提升了GMV预测效果。