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.

在线广告中的营销组合模型（MMM）以预测品牌店的总成交量（GMV）并帮助决策者调整各种广告渠道的预算分配。本文定义了一种新的因果性 MMM 问题，可以自动从数据中发现可解释的因果结构并产生更好的 GMV 预测。CausalMMM 集成 Granger 因果性在变分推断框架中，以度量不同渠道之间的因果关系，并以时间和饱和度营销响应模式的正则化预测 GMV。广泛的实验表明，CausalMMM 在合成数据集上的因果结构学习具有 5.7% 至 7.1% 的改进，并增强了代表性电子商务平台上的 GMV 预测结果。