Towards Generalizing Inferences from Trials to Target Populations
Melody Y Huang Sarah E Robertson Harsh Parikh
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.