  User marketing is a key focus of consumer-based internet companies. Learning
algorithms are effective to optimize marketing campaigns which increase user
engagement, and facilitates cross-marketing to related products. By attracting
users with rewards, marketing methods are effective to boost user activity in
the desired products. Rewards incur significant cost that can be off-set by
increase in future revenue. Most methodologies rely on churn predictions to
prevent losing users to make marketing decisions, which cannot capture up-lift
across counterfactual outcomes with business metrics. Other predictive models
are capable of estimating heterogeneous treatment effects, but fail to capture
the balance of cost versus benefit. We propose a treatment effect optimization
methodology for user marketing. This algorithm learns from past experiments and
utilizes novel optimization methods to optimize cost efficiency with respect to
user selection. The method optimizes decisions using deep learning optimization
models to treat and reward users, which is effective in producing
cost-effective, impactful marketing campaigns. Our methodology demonstrates
superior algorithmic flexibility with integration with deep learning methods
and dealing with business constraints. The effectiveness of our model surpasses
the quasi-oracle estimation (R-learner) model and causal forests. We also
established evaluation metrics that reflect the cost-efficiency and real-world
business value. Our proposed constrained and direct optimization algorithms
outperform by 24.6% compared with the best performing method in prior art and
baseline methods. The methodology is useful in many product scenarios such as
optimal treatment allocation and it has been deployed in production world-wide.
