How should causal predictions be transported when target populations differ in unit composition, evidence and mechanisms?
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?
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.
论文如何回答这个问题:形式化对象、论证 spine 与所需证据。
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.
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.
状态只记录可验证 delta:claim、SOTA opponent、theorem、experiment、manuscript 或 owner decision。
AAAI-27 abstract registered; full paper drafting.
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.
produce at least one case where composition correction succeeds and one where it must abstain due to mechanism change.
independently vary target unit proportions, evidence quality, support and within-unit mechanisms; compare pooled reweighting, domain adaptation and unit-centric transport.
selection diagrams/causal transportability; covariate and label shift; invariant and domain-adaptation methods.
research-questions/USL10-unit-centric-causal-transport/seed.mdpapers/USL10-unit-centric-causal-transport/paper.md
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