Causal inferences from a randomized controlled trial (RCT) may not pertain to
a target population where some effect modifiers have a different distribution.
Prior work studies generalizing the results of a trial to a target population
with no outcome but covariate data available. We show how the limited size of
trials makes generalization a statistically infeasible task, as it requires
estimating complex nuisance functions. We develop generalization algorithms
that supplement the trial data with a prediction model learned from an
additional observational study (OS), without making any assumptions on the OS.
We theoretically and empirically show that our methods facilitate better
generalization when the OS is high-quality, and remain robust when it is not,
and e.g., have unmeasured confounding.

给定一个随机对照试验，从中推理因果关系可能与一些效应修饰因子不同分布的目标人群无关。我们利用一个附加的观察研究学习到的预测模型，无需对该研究做任何假设来补充试验数据，提出了能够更好地推广试验结果的算法，并以理论和实证的方式展示了当观察研究是高质量时，我们的方法能够更好地推广，即使存在未测的混杂变量等情况仍能保持稳健。