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Jan Pedersen


Research Papers

Heterogeneous Causal Learning for Effectiveness Optimization in User Marketing

W. Y. Zou, S. Du, J. Lee, J. Pedersen
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. [...] [PDF]

Learning Continuous Treatment Policy and Bipartite Embeddings for Matching with Heterogeneous Causal Effects

W. Y. Zou, S. Shyam, M. Mui, M. Wang, J. Pedersen, Z. Ghahramani
Causal inference methods are widely applied in the fields of medicine, policy, and economics. Central to these applications is the estimation of treatment effects to make decisions. Current methods make binary yes-or-no decisions based on the treatment effect of a single outcome dimension. These methods are unable to capture continuous space treatment policies with a measure of intensity. [...] [PDF]

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