Causal Effect Estimation with Variational AutoEncoder and the Front Door Criterion
结合变分自编码器与前门准则的因果效应估计AI 解读 ↓
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结合变分自编码器与前门准则的因果效应估计
- 研究问题
- 在存在未观测混杂和中介时，能否通过表示学习获得可用于前门调整的信息，从观测数据中估计处理的因果效应？
- 方法与思路
- 作者提出 FDVAE，利用变分自编码器学习前门调整集的表示，替代直接搜索调整变量，并在合成及三个真实数据集上评估。
- 材料陈述的贡献
- 摘要报告合成实验中较好的比较表现，并称对混杂强度变化不敏感、可处理表示维数不匹配，真实数据应用展示了潜力。
- 阅读关注点
- 应核对所学表示满足前门条件的依据、模型训练目标和效应计算方式，以及模拟设计、比较基准与真实数据中的识别论证。
- 阅读边界
- 这是预印本，同行评审或期刊接收未核实；前门识别条件未说明，摘要关于 IV 必须没有中介的概括不能作为一般结论。
原文摘要
An essential problem in causal inference is estimating causal effects from observational data. The problem becomes more challenging with the presence of unobserved confounders. When there are unobserved confounders, the commonly used back-door adjustment is not applicable. Although the instrumental variable (IV) methods can deal with unobserved confounders, they all assume that the treatment directly affects the outcome, and there is no mediator between the treatment and the outcome. This paper aims to use the front-door criterion to address the challenging problem with the presence of unobserved confounders and mediators. In practice, it is often difficult to identify the set of variables used for front-door adjustment from data. By leveraging the ability of deep generative models in representation learning, we propose FDVAE to learn the representation of a Front-Door adjustment set with a Variational AutoEncoder, instead of trying to search for a set of variables for front-door adjustment. Extensive experiments on synthetic datasets validate the effectiveness of FDVAE and its superiority over existing methods. The experiments also show that the performance of FDVAE is not sensitive to the causal strength of unobserved confounders and is feasible in the case of dimensionality mismatch between learned representations and the ground truth. We further apply the method to three real-world datasets to demonstrate its potential applications.