USL10 Paper Portfolio
USL-10 Discovery · Transport AAAI-27 abstract registered · forum linked public contract: drafting

Unit-Centric Causal Transport: Separating Population Composition from Mechanism Change

How should causal predictions be transported when target populations differ in unit composition, evidence and mechanisms?

venue AAAI-27 Main Track topic ML: Transfer, Domain Adaptation & Continual Learning state abstract registered / writing owner gong · CausaClaw × DiscoSeed OpenReview forum ↗

基本研究问题

这篇论文独立可证伪的问题是什么;它与共享 ontology 中其他九篇不重叠的部分。

How should causal predictions be transported when target populations differ in unit composition, evidence and mechanisms?

核心 claim
Transport requires separating the Population conditionals that describe which actual units occur in each environment, what admissible evidence says about the selected unit, and whether fixed-unit mechanisms remain invariant.
如果成立会改变什么
External-validity failures could be attributed to composition, evidence, support or mechanism mismatch instead of one undifferentiated domain gap.
最强 reviewer 反对 / kill signal

反对:This is standard transportability, covariate shift or domain adaptation with latent classes.

Kill signal:kill if ordinary covariate overlap fully predicts performance and the unit decomposition never changes transport or abstention decisions.

基本思路

论文如何回答这个问题:形式化对象、论证 spine 与所需证据。

USL-01 type contract

Let $E$ be an environment variable in one Population world. Source and target are the events $\{E=s\}$ and $\{E=t\}$, so their compositions are written directly as $P(U\in du\mid E=s)$ and $P(U\in du\mid E=t)$ rather than as laws of separately indexed worlds. Given admissible evidence $\mathcal O$, the world conditional is $P(U\in du\mid E=e,\mathcal O)$ and a deployed learner forms $Q_\phi(du\mid E=e,\mathcal O)\approx P(U\in du\mid E=e,\mathcal O)$; the approximation is evaluated, not definitional. These environment-conditioned marginals do not by themselves couple individuals across environments: same-individual source/target records require reliable attribution or an explicit cross-environment linkage law. They are also not automatically observed-row laws: inclusion and observation intensity can induce a different event-weighted mixture, so the observation protocol and unit- versus event-weighted target must be declared. Composition shift concerns these Population conditionals; mechanism shift concerns separately declared fixed-individual response or potential-outcome laws and can occur even when the same individual values remain in support. Fixing $u$ does not remove event/exogenous variation. This decomposition does not itself identify a causal transport estimand; consistency, positivity, exchangeability/invariance and the admissible-evidence protocol must be stated separately. Identity support, response-law support and covariate support are distinct. A claim that transports to new Individuals must declare the cross-unit shared response family, property map, geometry or invariance that makes extrapolation possible, together with its support or abstention condition.

论证 spine

  1. Why a single domain-gap variable is insufficient.
  2. Source/target events in one Population law and their conditionals $P(U\in du\mid E=s)$ and $P(U\in du\mid E=t)$.
  3. World conditionals, learner selector beliefs and separately typed fixed-individual response laws.
  4. Causal target estimand and explicit transport-identification assumptions.
  5. Composition, evidence, support and mechanism failure certificates.
  6. Factorial transport benchmark and matched baselines.
  7. Partial transport, abstention and new-unit handling.

所需证据

  • Separate composition-correction and mechanism-change cases.
  • Comparison with reweighting, domain adaptation and causal transportability baselines.
  • An ablation showing why raw covariate overlap can rank targets incorrectly.
  • Selector-law and learner-belief diagnostics that do not infer causal identification from predictive calibration alone.
  • Separate evaluation of $Q_\phi(du\mid E=e,\mathcal O)$ as an approximation to, not a definition of, $P(U\in du\mid E=e,\mathcal O)$.
  • A declared cross-environment attribution/linkage regime; $P(U\in du\mid E=s)$ and $P(U\in du\mid E=t)$ alone do not identify which records concern the same individual.
  • Explicit consistency, positivity, exchangeability/invariance and evidence-admissibility assumptions, while retaining fixed-individual event/exogenous variation.
已注册 abstract(点击展开)

We transport causal predictions by separately modeling which actual units populate the target domain and whether their unit-conditioned mechanisms remain invariant. This separates composition shift, evidence shift, and mechanism change.

当前进展

状态只记录可验证 delta:claim、SOTA opponent、theorem、experiment、manuscript 或 owner decision。

当前状态

AAAI-27 abstract registered; full paper drafting.

下一写作门

formalize source/target as events in one Population law, write their unit conditionals and learner beliefs separately from fixed-individual response laws, and define the causal target estimand, transport/identification assumptions, four failure modes and abstention rule without treating target queries or outcomes as selector evidence.

下一证据门

produce at least one case where composition correction succeeds and one where it must abstain due to mechanism change.

48h 最小实验

independently vary target unit proportions, evidence quality, support and within-unit mechanisms; compare pooled reweighting, domain adaptation and unit-centric transport.

Closest SOTA opponents

selection diagrams/causal transportability; covariate and label shift; invariant and domain-adaptation methods.

canonical 文件

research-questions/USL10-unit-centric-causal-transport/seed.md
papers/USL10-unit-centric-causal-transport/paper.md

进展日志

2026-07-29 · AAAI-27 abstract registration 由 direct OpenReview forum link 验证;full paper drafting 启动;本页面建立为持续 review 面。

后续每次可验证 delta 追加在此;canonical 状态以 seed.md / paper.md / submission-ledger 为准,本页由 build_paper_pages.py 重新生成。