CausalMTA: Eliminating the User Confounding Bias for Causal Multi-touch Attribution

Multi-touch attribution (MTA), aiming to estimate the contribution of each advertisement touchpoint in conversion journeys, is essential for budget allocation and automatically advertising. Existing methods first train a model to predict the conversion probability of the advertisement journeys with historical data and calculate the attribution of each touchpoint by using the results counterfactual predictions. An assumption of these works is the conversion prediction model is unbiased. It can give accurate predictions on any randomly assigned journey, including both the factual and counterfactual ones. Nevertheless, this assumption does not always hold as the user preferences act as the common cause for both ad generation and user conversion, involving the confounding bias and leading to an out-of-distribution (OOD) problem in the counterfactual prediction. In this paper, we define the causal MTA task and propose CausalMTA to solve this problem. It systemically eliminates the confounding bias from both static and dynamic perspectives and learn an unbiased conversion prediction model using historical data. We also provide a theoretical analysis to prove the effectiveness of CausalMTA with sufficient ad journeys. Extensive experiments on both synthetic and real data in Alibaba advertising platform show that CausalMTA can not only achieve better prediction performance than the state-of-the-art method but also generate meaningful attribution credits across different advertising channels.