Methods for Causal Inference in Observational Studies

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Rowley, E. A., Mitchell, P. K., Yang, D.-H., Lewis, N., Dixon, B. E., Vazquez-Benitez, G., Fadel, W. F., Essien, I. J., Naleway, A. L., Stenehjem, E., Ong, T. C., Gaglani, M., Natarajan, K., Embi, P., Wiegand, R. E., Link-Gelles, R., Tenforde, M. W., & Fireman, B. (2025). Methods to Adjust for Confounding in Test-Negative Design COVID-19 Effectiveness Studies: Simulation Study. JMIR Formative Research, 9, e58981. https://doi.org/10.2196/58981
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Conover, M. M., Ryan, P. B., Chen, Y., Suchard, M. A., Hripcsak, G., & Schuemie, M. J. (2025). Objective study validity diagnostics: a framework requiring pre-specified, empirical verification to increase trust in the reliability of real-world evidence. Journal of the American Medical Informatics Association. https://doi.org/10.1093/jamia/ocae317
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Zhang, L., Richter, L. R., Wang, Y., Ostropolets, A., Elhadad, N., Blei, D. M., & Hripcsak, G. (2024). Causal fairness assessment of treatment allocation with electronic health records. Journal of Biomedical Informatics, 155, 104656. https://doi.org/10.1016/j.jbi.2024.104656
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Akkaya Hocagil, T., Ryan, L. M., Cook, R. J., Dang, K., Carter, R. C., Richardson, G. A., Day, N. L., Coles, C. D., Carmichael Olson, H., Jacobson, S. W., & Jacobson, J. L. (2024). Benchmark dose profiles for bivariate exposures. Risk Analysis. Portico. https://doi.org/10.1111/risa.14303
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Anand, T. V., Bu, F., Schuemie, M. J., Suchard, M. A., & Hripcsak, G. (2024). Comparative safety and effectiveness of angiotensin converting enzyme inhibitors and thiazides and thiazide‐like diuretics under strict monotherapy. The Journal of Clinical Hypertension, 26(4), 425–430. Portico. https://doi.org/10.1111/jch.14793
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Haug, M., Oja, M., Pajusalu, M., Mooses, K., Reisberg, S., Vilo, J., Giménez, A. F., Falconer, T., Danilović, A., Maljkovic, F., Dawoud, D., & Kolde, R. (2024). Markov modeling for cost-effectiveness using federated health data network. Journal of the American Medical Informatics Association, 31(5), 1093–1101. https://doi.org/10.1093/jamia/ocae044
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Kohli-Lynch, C., Thanassoulis, G., Pencina, M., Sehayek, D., Pencina, K., Moran, A., & Sniderman, A. D. (2024). The Causal-Benefit Model to Prevent Cardiovascular Events. JACC: Advances, 3(3), 100825. https://doi.org/10.1016/j.jacadv.2023.100825
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Brown, C. H., Hedeker, D., Gibbons, R. D., Duan, N., Almirall, D., Gallo, C., Burnett-Zeigler, I., Prado, G., Young, S. D., Valido, A., & Wyman, P. A. (2022). Accounting for Context in Randomized Trials after Assignment. Prevention Science, 23(8), 1321–1332. https://doi.org/10.1007/s11121-022-01426-9
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Zhang, L., Wang, Y., Schuemie, M. J., Blei, D. M., & Hripcsak, G. (2022). Adjusting for indirectly measured confounding using large-scale propensity score. Journal of Biomedical Informatics, 134, 104204. https://doi.org/10.1016/j.jbi.2022.104204
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Maisel-Campbell, A., Schlessinger, D. I., Yanes, A. F., Veledar, E., Reynolds, K. A., Ibrahim, S. A., Kang, B. Y., Anvery, N., Poon, E., & Alam, M. (2022). Voting behavior during FDA Medical Device Advisory Committee panel meetings. PLOS ONE, 17(6), e0267134. https://doi.org/10.1371/journal.pone.0267134
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