Learning to Infer Counterfactuals: Meta-Learning for Estimating Multiple Imbalanced Treatment Effects
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学习反事实推断：估计多种不平衡处理效应的元学习方法
- 研究问题
- 研究观察数据中处理类别多且样本量不平衡时，如何估计个体处理效应，尤其如何改善样本稀少处理组的反事实推断。
- 方法与思路
- 作者将处理组间的数据片段构造为元学习任务，从样本充足组学习并在稀少组更新，同时结合监督损失与潜在分布对齐损失。
- 材料陈述的贡献
- 摘要提出 MetaITE 框架，并称在两个真实数据集上，其推断准确性与泛化表现达到或超过所比较的方法，但未提供具体数值。
- 阅读关注点
- 应核对因果识别条件、处理组重叠程度、元任务构造，以及真实数据中的反事实评价依据和比较方法是否公平。
- 阅读边界
- 这是预印本，同行评审或期刊接收未核实。摘要未说明识别假设或评价真值来源，所述实验表现不能保证消除混杂或普遍优越。
原文摘要
We regularly consider answering counterfactual questions in practice, such as "Would people with diabetes take a turn for the better had they choose another medication?". Observational studies are growing in significance in answering such questions due to their widespread accumulation and comparatively easier acquisition than Randomized Control Trials (RCTs). Recently, some works have introduced representation learning and domain adaptation into counterfactual inference. However, most current works focus on the setting of binary treatments. None of them considers that different treatments' sample sizes are imbalanced, especially data examples in some treatment groups are relatively limited due to inherent user preference. In this paper, we design a new algorithmic framework for counterfactual inference, which brings an idea from Meta-learning for Estimating Individual Treatment Effects (MetaITE) to fill the above research gaps, especially considering multiple imbalanced treatments. Specifically, we regard data episodes among treatment groups in counterfactual inference as meta-learning tasks. We train a meta-learner from a set of source treatment groups with sufficient samples and update the model by gradient descent with limited samples in target treatment. Moreover, we introduce two complementary losses. One is the supervised loss on multiple source treatments. The other loss which aligns latent distributions among various treatment groups is proposed to reduce the discrepancy. We perform experiments on two real-world datasets to evaluate inference accuracy and generalization ability. Experimental results demonstrate that the model MetaITE matches/outperforms state-of-the-art methods.