Randomized Controlled Trials (RCTs) are pivotal in generating internally valid estimates with minimal assumptions, serving as a cornerstone for researchers dedicated to advancing causal inference methods. However, extending these findings beyond the experimental cohort to achieve externally valid estimates is crucial for broader scientific inquiry. This paper delves into the forefront of addressing these external validity challenges, encapsulating the essence of a multidisciplinary workshop held at the Institute for Computational and Experimental Research in Mathematics (ICERM), Brown University, in Fall 2023. The workshop congregated experts from diverse fields including social science, medicine, public health, statistics, computer science, and education, to tackle the unique obstacles each discipline faces in extrapolating experimental findings. Our study presents three key contributions: we integrate ongoing efforts, highlighting methodological synergies across fields; provide an exhaustive review of generalizability and transportability based on the workshop's discourse; and identify persistent hurdles while suggesting avenues for future research. By doing so, this paper aims to enhance the collective understanding of the generalizability and transportability of causal effects, fostering cross-disciplinary collaboration and offering valuable insights for researchers working on refining and applying causal inference methods.

翻译：随机对照试验（RCTs）对于在最小化假设条件下生成内部有效估计至关重要，是致力于推进因果推断方法的研究人员的基石。然而，将这些发现从实验队列推广出去以获得外部有效估计，对于更广泛的科学研究至关重要。本文深入探讨了应对外部有效性挑战的前沿进展，总结了2023年秋季在布朗大学计算与实验数学研究所（ICERM）举办的一次多学科研讨会的核心内容。该研讨会汇聚了来自社会科学、医学、公共卫生、统计学、计算机科学和教育学等多个领域的专家，共同探讨各学科在推广实验发现时所面临的独特障碍。我们的研究提出了三个关键贡献：整合了当前的研究努力，突出了跨领域的方法协同；基于研讨会讨论，对可推广性与可迁移性进行了详尽综述；识别了持续存在的障碍，并为未来研究提出了方向。通过以上工作，本文旨在增进对因果效应可推广性与可迁移性的集体理解，促进跨学科合作，并为致力于完善和应用因果推断方法的研究人员提供有价值的见解。