  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 using
counterfactual predictions. An assumption of these works is the conversion
prediction model is unbiased, i.e., 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 exposed
advertisements are recommended according to user preferences. This confounding
bias of users would lead to an out-of-distribution (OOD) problem in the
counterfactual prediction and cause concept drift in attribution. In this
paper, we define the causal MTA task and propose CausalMTA to eliminate the
influence of user preferences. It systemically eliminates the confounding bias
from both static and dynamic preferences to learn the conversion prediction
model using historical data. We also provide a theoretical analysis to prove
CausalMTA can learn an unbiased prediction model with sufficient data.
Extensive experiments on both public datasets and the impression data in an
e-commerce company show that CausalMTA not only achieves better prediction
performance than the state-of-the-art method but also generates meaningful
attribution credits across different advertising channels.
