Learning Invariant Representations under General Interventions on the Response
响应变量受到一般干预时的不变表示学习AI 解读 ↓
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响应变量受到一般干预时的不变表示学习
- 研究问题
- 当响应变量也受到干预、传统条件分布不变性失效时，能否找到其他不变关系，支持对未见环境的预测？
- 方法与思路
- 作者在线性结构因果模型中提出不变匹配性质，借助额外特征刻画干预，并通过变系数模型处理连续环境。
- 材料陈述的贡献
- 摘要称，该框架统一处理响应与预测变量上的一般干预，分析渐近泛化误差，并在包含新冠数据的实验中表现有竞争力。
- 阅读关注点
- 应核对不变匹配性质的精确定义、额外特征的构造与可用性，以及离散和连续环境下泛化误差分析的条件。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或期刊接收；摘要未说明完整识别条件，也未给出实验比较数值。
原文摘要
It has become increasingly common nowadays to collect observations of feature and response pairs from different environments. As a consequence, one has to apply learned predictors to data with a different distribution due to distribution shifts. One principled approach is to adopt the structural causal models to describe training and test models, following the invariance principle which says that the conditional distribution of the response given its predictors remains the same across environments. However, this principle might be violated in practical settings when the response is intervened. A natural question is whether it is still possible to identify other forms of invariance to facilitate prediction in unseen environments. To shed light on this challenging scenario, we focus on linear structural causal models (SCMs) and introduce invariant matching property (IMP), an explicit relation to capture interventions through an additional feature, leading to an alternative form of invariance that enables a unified treatment of general interventions on the response as well as the predictors. We analyze the asymptotic generalization errors of our method under both the discrete and continuous environment settings, where the continuous case is handled by relating it to the semiparametric varying coefficient models. We present algorithms that show competitive performance compared to existing methods over various experimental settings including a COVID dataset.