Experimental and observational studies often lack validity due to untestable assumptions. We propose a double machine learning approach to combine experimental and observational studies, allowing practitioners to test for assumption violations and estimate treatment effects consistently. Our framework proposes a falsification test for external validity and ignorability under milder assumptions. We provide consistent treatment effect estimators even when one of the assumptions is violated. However, our no-free-lunch theorem highlights the necessity of accurately identifying the violated assumption for consistent treatment effect estimation. Through comparative analyses, we show our framework's superiority over existing data fusion methods. The practical utility of our approach is further exemplified by three real-world case studies, underscoring its potential for widespread application in empirical research.

翻译：实验研究与观测研究常因无法验证的假设而缺乏有效性。我们提出一种双重机器学习方法来整合实验与观测研究，使研究者能够检验假设违反情况并一致地估计处理效应。该框架在较弱的假设条件下提出了针对外部有效性与可忽略性的证伪检验。即使其中一项假设被违反，我们仍能提供一致的处理效应估计量。然而，我们的"无免费午餐"定理强调，为获得一致的处理效应估计，必须准确识别被违反的假设。通过对比分析，我们证明了该框架相对于现有数据融合方法的优越性。三个真实案例研究进一步展示了本方法的实用价值，凸显了其在实证研究中广泛应用的潜力。