This paper presents a novel, causally-inspired approach to domain adaptation which aims to also include unlabelled data in the model fitting when labelled data is scarce. We consider a case of covariate-shift adaptation with cause and effect features, and--drawing from recent ideas in causal modelling and invariant prediction--show how this setting leads to, what we will refer to as, a semi-generative model: P(Y, X_eff; X_cau,\theta). Our proposed approach is robust to changes in the distribution over causal features, and naturally allows to impose model constraints by unsupervised learning of a map from causes to effects. In experiments on synthetic datasets we demonstrate a significant improvement in classification performance of our semi-generative model over purely-supervised and importance-weighting baselines when the amount of labelled data is small. Moreover, we apply our approach for regression on real-world protein-count data and compare it to feature transformation methods.

本文针对协变量偏移问题提出了一种半生成模型，该模型能够利用未标注的数据进行自适应学习和半监督学习，并且通过学习原因和影响同时作用的形式，解决了因果推理问题和领域转移问题，实验表明该方法在分类任务中具有显著优势。

半生成建模：基于因果关系特征的协变量偏移适应