Evaluations often inform future program implementation decisions. However, the implementation context may differ, sometimes substantially, from the evaluation study context. This difference leads to uncertainty regarding the relevance of evaluation findings to future decisions. Voluntary interventions pose another challenge to generalizability, as we do not know precisely who will volunteer for the intervention in the future. We present a novel approach for estimating target population average treatment effects among the treated by generalizing results from an observational study to projected volunteers within the target population (the treated group). Our estimation approach can accommodate flexible outcome regression estimators such as Bayesian Additive Regression Trees (BART) and Bayesian Causal Forests (BCF). Our generalizability approach incorporates uncertainty regarding target population treatment status into the posterior credible intervals to better reflect the uncertainty of scaling a voluntary intervention. In a simulation based on real data, we demonstrate that these flexible estimators (BCF and BART) improve performance over estimators that rely on parametric regressions. We use our approach to estimate impacts of scaling up Comprehensive Primary Care Plus, a health care payment model intended to improve quality and efficiency of primary care, and we demonstrate the promise of scaling to a targeted subgroup of practices.

翻译：评估通常为未来的项目实施方案提供信息。然而，实施背景可能与评估研究背景存在差异，有时甚至存在显著差异。这种差异导致评估结果对未来决策的相关性存在不确定性。自愿干预措施对普遍性提出了另一个挑战，因为我们无法准确预测未来谁将自愿参与干预。我们提出了一种新颖方法，通过将观察性研究的结果推广到目标人群中的潜在志愿者（即受干预群体），来估计受干预群体在目标人群中的平均处理效应。我们的估计方法能够容纳灵活的结果回归估计器，例如贝叶斯加性回归树（BART）和贝叶斯因果森林（BCF）。我们的普遍性方法将目标人群处理状态的不确定性纳入后验可信区间，以更准确地反映扩大自愿干预措施的不确定性。在一个基于真实数据的模拟中，我们证明了这些灵活估计器（BCF和BART）比依赖参数回归的估计器表现更优。我们使用该方法来估计扩大综合初级护理Plus（一种旨在提高初级护理质量和效率的医疗支付模式）的影响，并展示了将其推广到目标实践子群体的前景。