Causal Inference with Latent Variables: Recent Advances and Future Prospectives
含潜变量的因果推断：近期进展与未来展望AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要直接综述潜变量下的因果推断，并明确讨论图数据中的单位间干扰。
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含潜变量的因果推断：近期进展与未来展望
- 研究问题
- 混杂因素、中介或外生变量不可观测时，因果效应、机制和反事实分析面临哪些问题，已有方法如何应对这些潜变量？
- 方法与思路
- 作者按规避潜变量问题与推断潜变量两类策略组织综述，覆盖效应估计、中介、反事实及因果发现，并讨论图数据干扰。
- 材料陈述的贡献
- 摘要称系统梳理含潜变量的多类因果推断任务，将讨论扩展至单位间可能存在干扰的图数据，并提出大语言模型时代的方向。
- 阅读关注点
- 应核对各类潜变量与任务的对应关系、不同策略的识别条件，以及图数据中的干扰处理和语言模型机会的证据基础。
- 阅读边界
- 这是预印本，不能认定已同行评审或期刊接收；摘要未说明文献筛选标准，也未给出跨方法统一比较或通用识别保证。
原文摘要
Causality lays the foundation for the trajectory of our world. Causal inference (CI), which aims to infer intrinsic causal relations among variables of interest, has emerged as a crucial research topic. Nevertheless, the lack of observation of important variables (e.g., confounders, mediators, exogenous variables, etc.) severely compromises the reliability of CI methods. The issue may arise from the inherent difficulty in measuring the variables. Additionally, in observational studies where variables are passively recorded, certain covariates might be inadvertently omitted by the experimenter. Depending on the type of unobserved variables and the specific CI task, various consequences can be incurred if these latent variables are carelessly handled, such as biased estimation of causal effects, incomplete understanding of causal mechanisms, lack of individual-level causal consideration, etc. In this survey, we provide a comprehensive review of recent developments in CI with latent variables. We start by discussing traditional CI techniques when variables of interest are assumed to be fully observed. Afterward, under the taxonomy of circumvention and inference-based methods, we provide an in-depth discussion of various CI strategies to handle latent variables, covering the tasks of causal effect estimation, mediation analysis, counterfactual reasoning, and causal discovery. Furthermore, we generalize the discussion to graph data where interference among units may exist. Finally, we offer fresh aspects for further advancement of CI with latent variables, especially new opportunities in the era of large language models (LLMs).