---
title: "深度神经网络中遗漏变量偏差的控制方法"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2608.25930v1"
permalink: "https://feed.astralor.com/items/itm_2057e4e36eb5144377b3414c"
published: "2026-08-26T15:42:12.000Z"
score: 76
channel: "ai"
tags: ["广义可加模型","捷径学习","深度神经网络","神经影像","遗漏变量偏差"]
---

# 深度神经网络中遗漏变量偏差的控制方法

## AI 点评

> 这篇论文值得关注，因为它把统计学的控制变量思想真正落到了深度学习的可解释和公平性上。对任何在医学影像或社会计算中使用深度模型的人来说，这提供了一个比简单去相关更稳健的偏差校正工具。

## 速览

该论文指出，深度学习模型会从图像中推断人口统计学等协变量，并在预测时利用这些与结果相关的信息，形成一种称为“捷径学习”的遗漏变量偏差。现有公平性或混淆控制方法仅限制协变量与预测的关联，并不能真正纠正偏差。作者提出一种基于广义可加模型的控制变量方法，通过交叉拟合和岭惩罚对预训练网络的最终层进行重拟合，将协变量效应正交化并对该分布进行边缘化，从而得到无偏、可解释的预测。在模拟图像和真实神经影像数据上验证了方法的有效性。

## 关键点

- 现有混淆控制和公平性方法无法纠正遗漏变量偏差，因为它们只限制关联而非建模因果结构。
- 提出基于广义可加模型的控制变量方法，利用交叉拟合和岭惩罚对预训练网络最终层进行重拟合。
- 通过正交化协变量效应和边缘化处理，得到无偏、可解释的预测，并支持科学或公平性目标下的灵活性。
- 在模拟图像和真实神经影像数据上验证，方法能恢复真实效应，优于现有方法。

## 全文

Control variables are widely used in statistical modelling to account for omitted variable bias of known confounders. However, they have largely been underexplored in deep learning. This is surprising, given that deep learning models encode image-inferable covariates, such as demographic variables, into their predictions when these covariates are correlated with the outcome---a form of omitted variable bias referred to as 'shortcut learning'. While many existing confound-control or fairness methods try to restrict the correlation of such covariates with model predictions, we show that this fails to correct for omitted variable bias. We therefore propose a control variable approach for deep learning models, based on generalised additive modelling of the effects of model inputs and covariates. As flexible additive models can suffer from concurvity, we introduce an estimation procedure that refits the final layer of a pre-trained network to include covariate effects, using cross-fitting with ridge penalisation. We show how these effects can be orthogonalised with respect to covariates to exclude their mediated effects and that model predictions can be marginalised over the covariate distribution to control for their effect. This yields unbiased, interpretable predictions and offers flexibility to model the desired effects depending on the scientific or fairness objective. We verify our approach using simulated images, and demonstrate consistent estimation of true effects. Existing methods either require more data or fail to recover the true effects. We apply our method to real neuroimaging data with experimentally induced confounding, where it recovers prediction performance to near the level of a model trained on unconfounded data. Code is available at https://github.com/mpff/cocodeel.

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