Publications



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

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