Towards Generalizing Inferences from Trials to Target Populations

• URL: http://arxiv.org/abs/2402.17042v2
• Date: Sat, 25 May 2024 00:05:02 GMT
• Title: Towards Generalizing Inferences from Trials to Target Populations
• Authors: Melody Y Huang, Harsh Parikh,
• Abstract summary: This paper delves into the forefront of addressing external validity challenges, encapsulating a multidisciplinary workshop held at Brown University. Experts from diverse fields including social science, medicine, public health, statistics, computer science, and education, tackled the unique obstacles each discipline faces in extrapolating experimental findings. By doing so, this paper aims to enhance the collective understanding of the generalizability and transportability of causal effects.
• Score: 6.836945436656676
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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