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（RCT）以最少假设生成内部有效估计，是研究人员致力于推进因果推断方法的基石。然而，将实验队列的发现扩展至外部有效估计，对更广泛的科学探索至关重要。本文聚焦于解决这些外部有效性挑战的前沿问题，综合呈现了2023年秋季于布朗大学计算与实验数学研究所（ICERM）举办的多学科研讨会核心内容。该研讨会汇聚了社会科学、医学、公共卫生、统计学、计算机科学及教育学等领域的专家，共同探讨各学科在推断实验发现中面临的独特障碍。本研究提出三项核心贡献：整合现有研究进展，凸显跨领域的方法协同性；基于研讨会讨论内容，系统综述概化性与可迁移性研究；识别持续性难题并提出未来研究方向。通过上述工作，本文旨在深化对因果效应概化性与可迁移性的集体认知，促进跨学科合作，为改进和应用因果推断方法的研究者提供重要见解。