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Calibrated Inference for the Conditional Average Treatment Effect in the Few-Placebo Regime via Gaussian Processes

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arXiv:2605. 27473v1 Announce Type: new Abstract: Estimating how much an intervention helps a given individual the conditional average treatment effect (CATE) is increasingly central to decision-making in medicine, economics, and policy, where an estimate is most useful when accompanied by a calibrated uncertainty interval.

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Authors: Eichi Uehara
Released: 2026-05-28 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because Calibrated Inference for the Conditional Average Treatment Effect in the Few-Placebo Regime via Gaussian Processes overlaps with clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "Training one persona to emit a [ZLT] marker without bystanders adopting it has a one-cell-wide LR x epochs window on Qwen2.5-7B-Instruct (LOW confidence)", clean result "A pretraining-data-poisoned Qwen3-4B backdoor only fires on the exact trigger tokens — paraphrases don't activate it, and base-model similarity to the trigger doesn't predict which inputs fire (MODERATE confidence)". Matching terms: rect, under, rate, compare, stage, test, model. Source: arxiv stat.ML (Machine Learning).
arXiv: 2605.27473

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