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Counterfactual Prediction for Bundle Treatment
Hao Zou · Peng Cui · Bo Li · Zheyan Shen · Jianxin Ma · Hongxia Yang · Yue He

Wed Dec 09 09:00 AM -- 11:00 AM (PST) @ Poster Session 3 #787

Estimating counterfactual outcome of different treatments from observational data is an important problem to assist decision making in a variety of fields. Among the various forms of treatment specification, bundle treatment has been widely adopted in many scenarios, such as recommendation systems and online marketing. The bundle treatment usually can be abstracted as a high dimensional binary vector, which makes it more challenging for researchers to remove the confounding bias in observational data. In this work, we assume the existence of low dimensional latent structure underlying bundle treatment. Via the learned latent representations of treatments, we propose a novel variational sample re-weighting (VSR) method to eliminate confounding bias by decorrelating the treatments and confounders. Finally, we conduct extensive experiments to demonstrate that the predictive model trained on this re-weighted dataset can achieve more accurate counterfactual outcome prediction.

Author Information

Hao Zou (Tsinghua University)
Peng Cui (Tsinghua University)
Bo Li (Tsinghua University)
Zheyan Shen (Tsinghua University)
Jianxin Ma (Alibaba Group)
Hongxia Yang (Alibaba Group)
Yue He (Tsinghua University)

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