USL10 Paper Portfolio
USL-04 Counterfactual · Treatment Effect abstract Activity-verified · forum link pending · selector/property revision local only public contract: drafting Secondary deep-work lane

Treatment Effects from Unit Beliefs: Orthogonal Estimation Beyond Covariate Heterogeneity

Can pre-treatment which-individual beliefs improve treatment-effect estimation when they are pushed through a separately declared response-property or response-law geometry, rather than treating raw covariate neighborhoods or selector space itself as that geometry?

venue AAAI-27 Main Track topic ML: Causal Learning state abstract slot registered / selector property revision local and external sync unverified / writing owner gong · CausaClaw × DiscoSeed OpenReview forum 待回填

Registration boundary. The owner-side OpenReview Activity audit records the AAAI-27 abstract slot, while its direct OpenReview forum URL is still pending writeback. The current selector/property semantic revision is local and has not been verified against the external record.

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。

Can pre-treatment which-individual beliefs improve treatment-effect estimation when they are pushed through a separately declared response-property or response-law geometry, rather than treating raw covariate neighborhoods or selector space itself as that geometry?

核心 claim
Honest cross-fitted which-individual beliefs can be propagated through the whole treatment-response law selected by the same $u$, using a direct effect surface or optional derived response property/geometry to define localization weights, while orthogonal estimation separates selector-belief error from propensity, outcome-model and optional property-geometry error.
如果成立会改变什么
CATE/HTE methods could propagate distribution-valued uncertainty about which individual is involved through a response-relevant property map or kernel instead of treating either a point embedding or proximity in selector space as truth.
最强 reviewer 反对 / kill signal

反对:The unit belief is just another learned representation of `X`, so the estimator is an ordinary CATE learner.

Kill signal:downgrade to ordinary CATE if the composed belief-plus-property layer supplies no information beyond `X`/point properties, if no response-relevant geometry is declared, or if the estimand leaks post-treatment evidence.

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

The Unit Selection Variable $U\sim\Pi$ performs individual selection under the task-declared Population law $\Pi$. Pre-treatment evidence $\mathcal O^-$ first conditions the world law $P(U\in du\mid\mathcal O^-)$; the learner forms $Q_\phi(du\mid\mathcal O^-)\approx P(U\in du\mid\mathcal O^-)$ and must evaluate that approximation separately. Within the task-declared unit space, $u$ is the formal Individual and selects the whole treatment-response-law member, such as $P_\theta(dy\mid a,x,u,\mathcal O^-)$. This is a task-level modeling convention, not a metaphysical claim. The response interface is distinct from attribution, but the same $u$ indexes it directly; no second foundational representation is required. An application may optionally derive a property map $\rho:u\mapsto Z_u$ and use the pushforward $R_\phi(dz\mid\mathcal O^-)=\rho_{\#}Q_\phi(dz\mid\mathcal O^-)$, or define a distance between fixed-individual response laws. Neither $Q_\phi$ nor the measurable selector domain $\mathcal U$ supplies response geometry by itself. A response map may omit $\mathcal O^-$ only under a declared response-sufficiency assumption. All evidence in $\mathcal O^-$ must be pre-treatment, and this type split does not supply causal identification without explicit assignment, overlap, consistency and nuisance-function assumptions. The world causal estimand must be fixed before introducing $Q_\phi$; replacing the world conditional by a learner belief creates an estimator or approximation, not a new causal target unless a learner-relative decision target is explicitly declared.

论证 spine

  1. Covariate locality, individual identity and response-mechanism locality.
  2. Pre-treatment which-individual belief, the whole response law selected by $u$, optional response property/geometry, and target estimand.
  3. Pushforward or belief-integrated weights, orthogonal score, cross-fitting and uncertainty propagation.
  4. Support diagnostics and abstention.
  5. Matched synthetic benchmark plus standard HTE datasets.
  6. Comparison with causal forests, meta-learners and representation learners.

所需证据

  • A precise estimand and influence/orthogonality argument.
  • An explicit response interface such as $\tau(u,\mathcal O^-)$ or $P_\theta(dy\mid a,x,u,\mathcal O^-)$; any optional $Z_u$ or $d_R(u,v)$ must be derived or separately declared, and no distance on selector values may be called response geometry merely because $Q_\phi$ is distribution-valued. Omitting $\mathcal O^-$ requires response sufficiency.
  • Matched raw-X, point-property and full selector-belief-plus-property comparisons.
  • Negative controls showing no gain when the selector belief or the separately typed property layer carries no extra response information.
Current selector/property working abstract(尚未验证已同步外部记录)

We study treatment-effect estimation in which pre-treatment evidence $\mathcal O^-$ first defines the world conditional $P(U\in du\mid\mathcal O^-)$ over which actual individual is involved, and the learner forms $Q_\phi(du\mid\mathcal O^-)\approx P(U\in du\mid\mathcal O^-)$. Within the task-declared unit space, the formal value $u$ denotes the Individual and selects a whole treatment-response law; this is a modeling convention, not a metaphysical claim. The selector belief does not itself endow the individual domain with response geometry. Localization may act directly on a declared effect surface $\tau(u,\mathcal O^-)$ or response kernel, or optionally use a derived response property $Z_u$ or response-law distance $d_R(u,v)$, after which the belief can be pushed forward or integrated into weights. Omitting $\mathcal O^-$ from a response map requires a declared response-sufficiency assumption. The framework separates which-individual uncertainty, optional response-property uncertainty, treatment propensity, sample inclusion, and fixed-individual response variation.

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 slot registered; the selector/property wording below is

下一写作门

freeze the estimand, the whole response-law interface indexed by $u$, any optional derived response property or response-law distance, its relationship to $Q_\phi$, nuisance functions, cross-fitting protocol and abstention region; retain $\mathcal O^-$ unless response sufficiency is declared.

下一证据门

run a matched synthetic cell before adding IHDP/ACIC-style benchmarks.

48h 最小实验

compare raw-X neighborhoods, a point property embedding, and weights obtained by pushing distribution-valued selector beliefs through the same declared response geometry under matched cross-fitting on a DGP where covariate similarity, individual identity and response-mechanism similarity disagree.

Closest SOTA opponents

causal forests; DR/S/T/meta learners; representation-based HTE and latent-confounder models.

canonical 文件

research-questions/USL04-treatment-effects-from-unit-beliefs/seed.md
papers/USL04-treatment-effects-from-unit-beliefs/paper.md

进展日志

2026-07-29 · selector belief is now typed only as which-individual uncertainty; response geometry must come from a separately declared property, effect surface, kernel, or response-law distance. This local semantic revision has not been externally synchronized.

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。