Collaborative causal inference on distributed data
分布式数据上的协作因果推断AI 解读 ↓
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分布式数据上的协作因果推断
- 研究问题
- 当各方私有数据同时缺少样本和协变量时，如何通过数据协作改善治疗效应估计，并处理降维带来的信息损失？
- 方法与思路
- 作者提出 DC-QE，各方分享降维后的中间表示，在共享表示上估计倾向得分，再据此估计治疗效应。
- 材料陈述的贡献
- 摘要称，人工和真实数据实验中，多方协作的估计表现优于单独分析，协作收益能够抵消所观察到的降维损失。
- 阅读关注点
- 应核对跨方表示如何对齐、协变量不足如何补足、因果识别条件，以及共享表示的隐私保障和信息损失评估。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；摘要承认外部有效性未必成立，且未说明正式隐私保证。
原文摘要
In recent years, the development of technologies for causal inference with privacy preservation of distributed data has gained considerable attention. Many existing methods for distributed data focus on resolving the lack of subjects (samples) and can only reduce random errors in estimating treatment effects. In this study, we propose a data collaboration quasi-experiment (DC-QE) that resolves the lack of both subjects and covariates, reducing random errors and biases in the estimation. Our method involves constructing dimensionality-reduced intermediate representations from private data from local parties, sharing intermediate representations instead of private data for privacy preservation, estimating propensity scores from the shared intermediate representations, and finally, estimating the treatment effects from propensity scores. Through numerical experiments on both artificial and real-world data, we confirm that our method leads to better estimation results than individual analyses. While dimensionality reduction loses some information in the private data and causes performance degradation, we observe that sharing intermediate representations with many parties to resolve the lack of subjects and covariates sufficiently improves performance to overcome the degradation caused by dimensionality reduction. Although external validity is not necessarily guaranteed, our results suggest that DC-QE is a promising method. With the widespread use of our method, intermediate representations can be published as open data to help researchers find causalities and accumulate a knowledge base.