当前 claim
观测数据不是因果建模的本体起点,而是约束、校准和检验机制假设的证据。 如果真实世界的个体机制本来就是异质的,综述结构应从平均效应中心转向 heterogeneity-as-primitive 的对象边界。
这篇综述不从“给定一个数据表,估计一个效应”开始,而从一个更前置的问题开始: 因果问题如何先于观测数据存在,观测数据又如何约束、校准和检验机制假设? 当前稿件已进入 v0.7:完整 1-8 skeleton、第一轮 citation/prose pressure、 Section 6 worked boundary example,以及第一张 question-first evidence bridge。
观测数据不是因果建模的本体起点,而是约束、校准和检验机制假设的证据。 如果真实世界的个体机制本来就是异质的,综述结构应从平均效应中心转向 heterogeneity-as-primitive 的对象边界。
做 public-facing readability polish,把 Figure 1 投影到 website / slide / LaTeX 格式,并继续保持 baseline-first:只有 causal artifact 改变时才使用 HCGM / DiscoSCM 作为主 claim。
这个页面是 public-safe projection,不发布内部草稿全文、私密 Discord 协作记录、 reviewer strategy 或 local-only source paths。事实源仍在项目 workspace 的 P01 artifacts 和 portfolio state 中。
P01 的第一张图把读者从“观测数据”带回“因果问题”:数据进入以后只是 evidence bridge, 不是因果关系本身。HCGM / DiscoSCM 只有在 artifact 或 decision 改变时才进入主 claim。 这里的 HCGM 比较是 object-level:标准 SCM 共享模型定义层 assignment,固定 exogenous state 后可诱导 unit-specific slice;HCGM / Unit Mechanism Learning 把 unit-specific mechanism 本身设为 primary object,并用 shared unit-modulated family 使它可学习,而不是声称 SCM 无法表达异质响应。
| Layer | Reader question | Evidence role | Failure test |
|---|---|---|---|
| Causal question before data | What are we trying to explain, change, diagnose, or transport? | World knowledge and counterfactual imagination define the candidate question. | If the question is unclear, method choice is premature. |
| Observed world | What did the table actually record? | Sampling, selection, measurement, assignment, policy history, and behavior project the generated world into data. | If the projection is ignored, association is mistaken for causal evidence. |
| Baseline causal artifact | Which existing artifact already answers the question? | PO, SCM/DAG, IV/LATE/LDTE, CATE/HTE, transportability, CRL, or dynamics may already be sufficient. | If the baseline gives the same decision, do not invoke HCGM / DiscoSCM as the main claim. |
| Primitive heterogeneity pressure | Which object remains unrepresented? | Unit type, mechanism regime, event noise, context, measurement, or target-world relation may remain active. | The added object must change the query, assumptions, failure mode, repair, or decision. |
这些不是泛泛引用,而是从 top-ai-causality intake module 回流到 P01 的 reviewer pressure。
NeurIPS 2025 `Beyond the Average` 让 P01 必须尊重 LDTE / IV / LATE baseline; 它是 Section 6 的主 worked boundary example,而不是被 HCGM / DiscoSCM 直接替代。
ICML 2025 `Counterfactual Graphical Models` 压住一个边界:如果现有 counterfactual graph machinery 已经能表达目标 query,P01 就不能把 DiscoSCM 写成笼统替代。
ICML 2025 CRL sanity check、NeurIPS 2025 CausalVerse 和 CausalDynamics 共同要求: representation、simulator ground truth、dynamics 和 lag structure 必须进入 evidence standard。
FANS 是最强的未来 mechanism-shift second-example candidate,但在 final paper body / stable metadata / changed artifact 捕获前,先保留为 pressure lead。
如果目标是对外可读,继续 P01 readability polish 和 projection;如果目标是 novelty evidence,切去 P17 的 benchmark-style comparison。当前默认仍是先让 P01 成为可展示 review wedge。