Stable Prediction across Unknown Environments

TL;DR

This paper introduces a novel deep learning approach combining auto-encoders and global balancing to achieve stable predictions across unknown environments, addressing distribution shifts without prior knowledge of test distributions.

Contribution

The paper proposes the Deep Global Balancing Regression (DGBR) algorithm, integrating feature selection and balancing weights to identify stable, causal feature-outcome relationships in high-dimensional data.

Findings

DGBR outperforms existing methods on synthetic datasets.

DGBR achieves more stable predictions across unknown environments.

Theoretical guarantees support the effectiveness of DGBR.

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.