arXiv 26 Feb 2024 · Statistics — Methodology · 2 citations (OpenAlex)
arXiv:2402.17042 · PDF · DOI · OpenAlex · Extracted main text
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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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Huang, M (2022) Sensitivity analysis in the generalization of experimental results self | 0.843 | 4 | 3 | 75% |
| 2 | Dahabreh, I. J., Robertson, S. E., and Hernán, M. A (2022) Generalizing and transporting inferences about the effects of treatment assignment subject to non-adherence | 0.830 | 7 | 2 | 86% |
| 3 | Huling, J. H (2023) Transportability of prinicipal causal effects | 0.737 | 4 | 2 | 75% |
| 4 | Rudolph, Williams, N. T., Stuart, E. A., and Diaz, I (2023) Efficiently transporting average treatment effects using a sufficient subset of effect modifiers | 0.737 | 3 | 3 | 67% |
| 5 | Tipton, E. and Mamakos, M (2023) Designing randomized experiments to predict unit-specific treatment effects | 0.737 | 3 | 3 | 67% |
| 6 | Zivich, P. N., Edwards, J. K., Shook-Sa, B. E., Lofgren, E. T., Less… (2023) Synthesis estimators for positivity violations with a continuous covariate | 0.737 | 3 | 3 | 67% |
| 7 | Cheng, Y., Wu, L., and Yang, S (2023) Enhancing treatment effect estimation: A model robust approach integrating randomized experiments and external controls using th… | 0.644 | 3 | 2 | 67% |
| 8 | Schnitzler, N. and Kaizar, E (2023) A two-stage method for extending inferences from a collection of trials | 0.644 | 3 | 2 | 67% |
| 9 | Chipman, H. A., George, E. I., and McCulloch, R (2007) Bayesian ensemble learning | 0.644 | 2 | 2 | 100% |
| 10 | Dahabreh, I. J., Robertson, S. E., Tchetgen, E. J., Stuart, E. A., a… (2019) Generalizing causal inferences from individuals in randomized trials to all trial-eligible individuals | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 44 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | A Cautionary Tale on Integrating Studies with Disparate Outcome Measures for Causal Inference | 0.644 | 2 | 2 |
| 2 | TEA-Time: Transporting Effects Across Time | 0.405 | 1 | 1 |