Causal Inference on Process Graphs, Part II: Causal Structure and Effect Identification
过程图上的因果推断（二）：因果结构与效应识别AI 解读 ↓
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过程图上的因果推断（二）：因果结构与效应识别
- 研究问题
- 能否从 SVAR 过程的谱密度恢复过程层面的因果结构，并识别受到外生潜在过程混杂的频域因果效应？
- 方法与思路
- 作者通过谱密度的代数约束识别过程图的 d 与 t 分离信息，定义有理可识别性，并运用潜在因子半 trek 图准则。
- 材料陈述的贡献
- 摘要称分离信息可通过谱密度约束识别，且图准则可判断某些混杂效应能否由谱密度元素的有理运算恢复。
- 阅读关注点
- 应核对 SVAR 模型条件、两类分离与谱约束的对应关系，以及有理可识别性和半 trek 准则的适用范围。
- 阅读边界
- 这是预印本，同行评审或期刊接收未核实。摘要未列明完整图与模型条件，不能推断任意过程图或混杂效应都可识别。
原文摘要
A structural vector autoregressive (SVAR) process is a linear causal model for variables that evolve over a discrete set of time points and between which there may be lagged and instantaneous effects. The qualitative causal structure of an SVAR process can be represented by its finite and directed process graph, in which a directed link connects two processes whenever there is a lagged or instantaneous effect between them. At the process graph level, the causal structure of SVAR processes is compactly parameterised in the frequency domain. In this paper, we consider the problem of causal discovery and causal effect estimation from the spectral density, the frequency domain analogue of the auto covariance, of the SVAR process. Causal discovery concerns the recovery of the process graph and causal effect estimation concerns the identification and estimation of causal effects in the frequency domain. We show that information about the process graph, in terms of $d$- and $t$-separation statements, can be identified by verifying algebraic constraints on the spectral density. Furthermore, we introduce a notion of rational identifiability for frequency causal effects that may be confounded by exogenous latent processes, and show that the recent graphical latent factor half-trek criterion can be used on the process graph to assess whether a given (confounded) effect can be identified by rational operations on the entries of the spectral density.