B-CALM: Bias-Limited Bayesian Borrowing for RCT-Anchored Treatment Effects under Covariate Mismatch
B-CALM：协变量不匹配下以随机试验为锚的偏差受限贝叶斯信息借用AI 解读 ↓
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B-CALM：协变量不匹配下以随机试验为锚的偏差受限贝叶斯信息借用
- 研究问题
- 随机试验难以精确估计治疗异质性时，如何借用存在混杂且协变量仅部分重叠的观察数据，估计试验人群的条件治疗效应？
- 方法与思路
- 作者将不同来源协变量映射至共享潜在状态，联合建模结局面，以基线偏差和比较偏差函数校准观察数据的信息借用。
- 材料陈述的贡献
- 摘要称，有限特征下观察数据的信息贡献受比较偏差先验精度限制，并随样本增长饱和；实验报告近名义平均覆盖。
- 阅读关注点
- 应核对潜在对齐与试验目标的识别关系、比较偏差先验的敏感性，以及信息界、有效样本量和风险界各自的适用条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未说明完整识别条件，平均覆盖表现也不能保证逐点覆盖。
原文摘要
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.