Abstract: The marketing effect estimation is an important issue for precision marketing. Although the causal effect estimation has provided the research framework for this problem, the existing method mainly focuses on the coarse-grained causal effect estimation on a group of users. When dealing with the fine-grained evaluation problem on an individual user, the existing methods usually fail to solve the challenges raised by the user temporal feature modeling and high dimensional confounder selection. To address the above challenges, a fine-grained deep confounder balancing marketing effect estimation method is proposed. Firstly, a long and short memory network is introduced to model the users’ temporal features. Secondly, a neural network with multi-sparse-connected layers is devised to construct the confounders balancing weight network for both temporal and non-temporal features. Finally, the marketing effect is evaluated using the balancing weight learned in the previous stage.It shows that the time feature modeling and nearest neighbor matching ideas proposed in this paper can reduce the deviation of the effect estimation and improve the stability, it has reference significance for the optimization of marketing strategy.