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Counterfactual Prediction for Outcome-Oriented Treatments
Hao Zou · Bo Li · Jiangang Han · Shuiping Chen · Xuetao Ding · Peng Cui

Thu Jul 21 01:05 PM -- 01:10 PM (PDT) @ Room 318 - 320

Large amounts of efforts have been devoted into learning counterfactual treatment outcome under various settings, including binary/continuous/multiple treatments. Most of these literature aims to minimize the estimation error of counterfactual outcome for the whole treatment space. However, in most scenarios when the counterfactual prediction model is utilized to assist decision-making, people are only concerned with the small fraction of treatments that can potentially induce superior outcome (i.e. outcome-oriented treatments). This gap of objective is even more severe when the number of possible treatments is large, for example under the continuous treatment setting. To overcome it, we establish a new objective of optimizing counterfactual prediction on outcome-oriented treatments, propose a novel Outcome-Oriented Sample Re-weighting (OOSR) method to make the predictive model concentrate more on outcome-oriented treatments, and theoretically analyze that our method can improve treatment selection towards the optimal one. Extensive experimental results on both synthetic datasets and semi-synthetic datasets demonstrate the effectiveness of our method.

Author Information

Hao Zou (Tsinghua University)
Bo Li (Tsinghua University)
Jiangang Han (Meituan)
Shuiping Chen (Meituan)
Xuetao Ding (meituan)
Peng Cui (Tsinghua University)
Peng Cui

Peng Cui is an Associate Professor in Tsinghua University. He got his PhD degree from Tsinghua University in 2010. His research interests include causal inference and stable learning, network representation learning, and human behavioral modeling. He has published more than 100 papers in prestigious conferences and journals in data mining and multimedia. His recent research won the IEEE Multimedia Best Department Paper Award, SIGKDD 2016 Best Paper Finalist, ICDM 2015 Best Student Paper Award, SIGKDD 2014 Best Paper Finalist, IEEE ICME 2014 Best Paper Award, ACM MM12 Grand Challenge Multimodal Award, and MMM13 Best Paper Award. He is the Associate Editors of IEEE TKDE, IEEE TBD, ACM TIST, and ACM TOMM etc. He has served as program co-chair and area chair of several major machine learning and artificial intelligence conferences, such as IJCAI, AAAI, ACM CIKM, ACM Multimedia etc.

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