Incorporating Interventional Independence Improves Robustness against Interventional Distribution Shift
利用干预独立性提高对干预分布偏移的稳健性AI 解读 ↓
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利用干预独立性提高对干预分布偏移的稳健性
- 研究问题
- 已知潜在变量的因果图且干预训练样本有限时，如何学习在干预分布变化下仍具有较稳定预测表现的判别性表示？
- 方法与思路
- 作者分析干预诱导的独立性与预测误差差异，为线性模型推导充分条件，并提出显式约束干预节点与其非后代表示独立的 RepLIn。
- 材料陈述的贡献
- 摘要称独立性约束在特定线性条件下可降低干预测试误差，并在合成数据及具有半合成因果结构的图像、文本任务中展示算法效果。
- 阅读关注点
- 应核对已知因果图及定向干预的具体设定、线性结果要求的干预样本比例，以及独立性约束如何估计和用于非线性表示训练。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收均未核实。摘要未给出充分条件细节；半合成结构实验不能直接验证真实未知因果图下的稳健性。
原文摘要
We study the problem of learning robust discriminative representations of causally related latent variables given the underlying causal graph and a training set comprising passively collected observational data and interventional data obtained through targeted interventions on some of these latent variables. We desire to learn representations that are robust against the resulting interventional distribution shifts. Existing approaches treat observational and interventional data alike, ignoring the independence relations arising from these interventions, even with known underlying causal models. As a result, their representations lead to large predictive performance disparities between observational and interventional data. This performance disparity worsens when interventional training data is scarce. In this paper, (1) we first identify a strong correlation between this performance disparity and the representations' violation of statistical independence induced during interventions. (2) For linear models, we derive sufficient conditions on the proportion of interventional training data, for which enforcing statistical independence between representations of the intervened node and its non-descendants during interventions lowers the test-time error on interventional data. Combining these insights, (3) we propose RepLIn, a training algorithm that explicitly enforces this statistical independence between interventional representations. We demonstrate the utility of RepLIn on a synthetic dataset, and on real image and text datasets on facial attribute classification and toxicity detection, respectively, with semi-synthetic causal structures. Our experiments show that RepLIn is scalable with the number of nodes in the causal graph and is suitable to improve robustness against interventional distribution shifts of both continuous and discrete latent variables compared to the ERM baselines.