2025
Frank, K. A. (2025). Sensitivity to attrition for inferences from an RCT. Paper presented at the Meeting of the Society for Causal Inference (Detroit, MI, May 2025). Web
Frank, K. A., Lin, Q., & Maroulis, S., Dai, S., Jess, N., Lin, H.-C., Liu, Y., Maestrales, S., Searle, E., & Tait, J. (2025). Quantifying sensitivity to selection on unobserved covariates: Recasting the coefficient of proportionality within a correlational framework. Preprint. Web
Xu, R., Frank, K. A., Lin, Q. Maroulis, S. J., & Cheng, X. (2025). Quantifying the robustness of inferences to replacement of data for main effects and moderators: Application to a tier 2 literacy intervention. Paper presented at the Meeting of the Society for Causal Inference (Detroit, MI, May 2025). Web
2024
Narvaiz, S., Lin, Q., Rosenberg, J. M., Frank, K. A., Maroulis, S. J., Wang, W., & Xu, R. (2024). konfound: An R sensitivity analysis package to quantify the robustness of causal inferences. Journal of Open Source Software, 9(95), 5779. Web
2023
Frank, K. A., Lin, Q., & Maroulis, S. (2023). Quantifying sensitivity to selection on unobservables: Refining Oster’s coefficient of proportionality. White paper. PDF
Frank, K. A., Lin, Q., Xu, R., Maroulis, S. J., Mueller, A. (2023). Quantifying the robustness of causal inferences: Sensitivity analysis for pragmatic social science. Social Science Research, 110, 102815. PDF
Lin, Q., Nuttall, A., Zhang, Q., Frank, K.A. (2023) How do unobserved confounding mediators and measurement error impact estimated mediation effects and corresponding statistical inferences? Introducing R Package ConMed for sensitivity analysis. Psychological Methods, 28(2), 339-358. Web
2022
Li, T., & Frank, K. (2022). The probability of a robust inference for internal validity. Sociological Methods & Research, 51(4), 1947-1968. Web
2021
Frank, K. A., Lin, Q., Maroulis, S., Mueller, A. S., Xu, R., Rosenberg, J. M., Hayter, C. S., Mahmoud, R. A., Kolak, M., Dietz, T., & Zhang, L. (2021). Hypothetical case replacement can be used to quantify the robustness of trial results. Journal of Clinical Epidemiology, 134, 150-159. (authors listed alphabetically.) PDF | Web
2019
Xu, R., Frank, K. A., Maroulis, S. J., & Rosenberg, J. M. (2019). konfound: Command to quantify robustness of causal inferences. The Stata Journal, 19(3), 523–550. PDF | Web
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2013
Frank, K. A., Maroulis, S. J., Duong, M. Q., & Kelcey, B. M. (2013). What would it take to change an inference? Using Rubin’s causal model to interpret the robustness of causal inferences. Educational Evaluation and Policy Analysis, 35(4), 437-460. PDF | Web
2009
Pan, W. (2009). A SAS/IML macro for computing percentage points of Pearson distributions. Journal of Statistical Software, 31(Code Snippet 2), 1-6. Web
2008
Frank, K. A., Sykes, G., Anagnostopoulos, D., Cannata, M., Chard, L., Krause, A., & McCrory, R. (2008). Does NBPTS certification affect the number of colleagues a teacher helps with instructional matters?. Educational Evaluation and Policy Analysis, 30(1), 3-30. PDF | Web
2004
Pan, W., & Frank, K. A. (2004). An approximation to the distribution of the product of two dependent correlation coefficients. Journal of Statistical Computation and Simulation, 74(6), 419-443. Web
2003
Pan, W., & Frank, K. A. (2003). A probability index of the robustness of a causal inference. Journal of Educational and Behavioral Statistics, 28(4), 315-337. Web
2000
Frank, K. (2000). Impact of a confounding variable on the inference of a regression coefficient. Sociological Methods and Research, 29(2), 147-194. PDF | Web