H
Hao Zou
Researcher at Tsinghua University
Publications - 21
Citations - 120
Hao Zou is an academic researcher from Tsinghua University. The author has contributed to research in topics: Computer science & Chemistry. The author has an hindex of 3, co-authored 6 publications receiving 43 citations.
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Proceedings Article
Counterfactual Prediction for Bundle Treatment
TL;DR: This work proposes a novel variational sample re-weighting (VSR) method to eliminate confounding bias by decorrelating the treatments and confounders and conducts extensive experiments to demonstrate that the predictive model trained on this re-weightsed dataset can achieve more accurate counterfactual outcome prediction.
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Physical Co-Design of Flow and Control Layers for Flow-Based Microfluidic Biochips
TL;DR: A novel integrated physical co-design methodology, which seamlessly integrates the flow-layer and control-layer design stages, is presented, which allows for an iterative placement refinement based on routing feedbacks.
Proceedings ArticleDOI
Focused Context Balancing for Robust Offline Policy Evaluation
TL;DR: This paper proposes a non-parametric method, named Focused Context Balancing (FCB) algorithm, to learn sample weights for context balancing, so that the distribution shift induced by the past policy and new policy can be eliminated respectively.
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CausPref: Causal Preference Learning for Out-of-Distribution Recommendation
TL;DR: This work makes a thorough analysis of implicit recommendation problem from the viewpoint of out-of-distribution (OOD) generalization, and proposes to incorporate the recommendation-specific DAG learner into a novel causal preference-based recommendation framework named CausPref, mainly consisting of causal learning of invariant user preference and anti-preference negative sampling to deal with implicit feedback.
Journal ArticleDOI
Data-Driven Variable Decomposition for Treatment Effect Estimation
TL;DR: This paper proposes a Data-Driven Variable Decomposition algorithm, which can automatically separate confounders and adjustment variables with a data-driven approach, and simultaneously estimate treatment effect in observational studies with high dimensional variables.