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Stable Predictions across Unknown Environments

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  • Kuang, Kun

    (Tsinghua University)

  • Xiong, Ruoxuan

    (Stanford University)

  • Cui, Peng

    (Tsinghua University)

  • Athey, Susan

    (Stanford University)

  • Li, Bo

    (Tsinghua University)

Abstract

In many important machine learning applications, the training distribution used to learn a probabilistic classifier differs from the testing distribution on which the classifier will be used to make predictions. Traditional methods correct the distribution shift by reweighting the training data with the ratio of the density between test and training data. In many applications training takes place without prior knowledge of the testing distribution on which the algorithm will be applied in the future. Recently, methods have been proposed to address the shift by learning causal structure, but those methods rely on the diversity of multiple training data to a good performance, and have complexity limitations in high dimensions. In this paper, we propose a novel Deep Global Balancing Regression (DGBR) algorithm to jointly optimize a deep auto-encoder model for feature selection and a global balancing model for stable prediction across unknown environments. The global balancing model constructs balancing weights that facilitate estimating of partial effects of features (holding fixed all other features), a problem that is challenging in high dimensions, and thus helps to identify stable, causal relationships between features and outcomes. The deep auto-encoder model is designed to reduce the dimensionality of the feature space, thus making global balancing easier. We show, both theoretically and with empirical experiments, that our algorithm can make stable predictions across unknown environments. Our experiments on both synthetic and real world datasets demonstrate that our DGBR algorithm outperforms the state-of-the-art methods for stable prediction across unknown environments.

Suggested Citation

  • Kuang, Kun & Xiong, Ruoxuan & Cui, Peng & Athey, Susan & Li, Bo, 2018. "Stable Predictions across Unknown Environments," Research Papers 3695, Stanford University, Graduate School of Business.
  • Handle: RePEc:ecl:stabus:3695
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    Cited by:

    1. Liu, Zhi & Zheng, Xiao-Xue & Li, Deng-Feng & Liao, Chen-Nan & Sheu, Jiuh-Biing, 2021. "A novel cooperative game-based method to coordinate a sustainable supply chain under psychological uncertainty in fairness concerns," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 147(C).
    2. Michael Gechter & Keisuke Hirano & Jean Lee & Mahreen Mahmud & Orville Mondal & Jonathan Morduch & Saravana Ravindran & Abu S. Shonchoy, 2024. "Selecting Experimental Sites for External Validity," Papers 2405.13241, arXiv.org.
    3. Qiang Liu & Yingtao Luo & Shu Wu & Zhen Zhang & Xiangnan Yue & Hong Jin & Liang Wang, 2022. "RMT-Net: Reject-aware Multi-Task Network for Modeling Missing-not-at-random Data in Financial Credit Scoring," Papers 2206.00568, arXiv.org.

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