AI 中文总结
研究针对随机对照试验样本小、观察性研究有混杂且协变量部分重叠问题，开发B-CALM方法估计RCT定义的条件平均治疗效果，有偏差函数及理论推导，在多类研究中效果良好。
AI 中文摘要
随机对照试验（RCTs）可确定随机试验人群中的治疗效果，但往往规模太小无法进行可靠的异质性估计；观察性研究（OS）规模较大但存在混杂且仅在部分重叠的协变量上进行测量。我们开发了协变量不匹配下的贝叶斯校准对齐（B-CALM），一种用于RCT定义的条件平均治疗效果（CATE）估计的贝叶斯借用方法。B-CALM将特定来源的协变量映射到共享的潜在状态，联合建模试验和观察结果表面，并使用基线偏差和比较偏差函数来表示OS与试验估计量的差异。比较偏差先验成为一个明确的敏感性旋钮：我们证明了一个有限特征偏差受限信息界，表明关于试验治疗效果函数的观察对比信息由该偏差函数的先验精度限制，并推导了一个有效样本量公式，表明随着OS样本量的增加，OS贡献的RCT等效信息会饱和。该理论还将PAC-贝叶斯试验风险界与积分概率度量（IPM）对齐和校准分解相结合，该分解将RCT经验风险、潜在对齐和去偏OS表面的残余校准分开。在合成、半合成和儿科肥胖外部对照研究中，B-CALM保持接近名义的平均覆盖率和低负迁移，而合并和因果森林基线在比较偏差下可能会变得过于自信。
英文摘要
Randomized controlled trials (RCTs) identify treatment effects in the randomized trial population but are often too small for reliable heterogeneity estimation; observational studies (OS) are larger but confounded and measured on only partially overlapping covariates. We develop Bayesian Calibrated ALignment under covariate Mismatch (B-CALM), a Bayesian borrowing method for RCT-defined conditional average treatment effect (CATE) estimation. B-CALM maps source-specific covariates into a shared latent state, jointly models trial and observational outcome surfaces, and uses baseline-bias and comparative-bias functions to represent how the OS departs from the trial estimand. The comparative-bias prior becomes an explicit sensitivity knob: we prove a finite-feature bias-limited information bound showing that observational contrast information about the trial treatment-effect function is capped by the prior precision of this bias function, and derive an effective-sample-size formula showing that the RCT-equivalent information contributed by the OS saturates as OS sample size grows. The theory also combines a PAC-Bayes trial-risk bound with an integral-probability-metric (IPM) alignment and calibration decomposition that separates RCT empirical risk, latent alignment, and residual calibration of the debiased OS surface. In synthetic, semi-synthetic, and pediatric-obesity external-control studies, B-CALM maintains near-nominal average coverage and low negative transfer while pooled and causal-forest baselines can become overconfident under comparative bias.
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