In observational causal inference, domain knowledge often leaves multiple covariate adjustments plausible, yet which sets satisfy ignorability is untestable. Different adjustment sets can yield conflicting estimates of the average treatment effect, and standard remedies (adjusting for their union or intersection, or reporting the union or convex hull of confidence intervals) can fail or produce intervals whose width does not vanish with sample size. We propose a specification-robust procedure that returns a single point estimate and a confidence interval that is valid as long as at least one candidate adjustment set is valid and has width shrinking at the parametric $n^{-1/2}$ rate. Our approach mirrors how trimming and overlap weighting handle overlap violations:~We shift the target to a reweighted population, closest in KL-divergence to the original population, for which credible, specification-robust inference is feasible. We also provide diagnostic plots to assess the population shift and an extension to protect any function of the covariates used for reweighting, similar to calipers in matching. Synthetic and real-data examples demonstrate that our procedure provides substantially tighter confidence intervals than the convex hull while maintaining nominal coverage.

翻译：在观察性因果推断中，领域知识常使得多种协变量调整方案均看似合理，然而哪些变量集合满足可忽略性假设却不可检验。不同调整集可能导致平均处理效应的估计存在冲突，而标准补救措施（调整并集或交集、报告置信区间并集或凸包）可能失效，或产生宽度不随样本量收缩的区间。我们提出一种规范稳健的流程，该方法返回单个点估计和置信区间，只要至少一个候选调整集有效且其宽度以参数速率$n^{-1/2}$衰减，该区间即保持有效性。我们的方法借鉴了修剪与重叠加权处理重叠违规的思路：将目标转向重加权总体（该总体在KL散度上最接近原始总体），使其能实现可信的规范稳健推断。我们还提供诊断图以评估总体偏移，并扩展出保护机制（类似匹配中的卡钳）以维护任何用于重加权的协变量函数。合成数据与真实数据示例表明，该方法在保持名义覆盖水平的同时，能提供比凸包显著更窄的置信区间。