All of the assumptions of statistical analysis rarely hold. The challenge for the pragmatist is understanding when evidence is strong enough to support action—and that is where sensitivity analysis comes in, allowing us to understand how robust our inferences are to challenges to our assumptions.
Example Statement: XX% of the estimated effect would have to be due to bias to change your inference about the effect.
Our work in the KonFound-It! project is to develop—and make easy to use—sensitivity analyses that quantify the robustness of inferences to concerns about omitted variables and other sources of bias.
Reach out through the Google Group Forum →Learn More About Sensitivity Analysis
AI-Generated Podcast
Listen to an audio discussion created by Google’s NotebookLM exploring our team’s article, “Quantifying the robustness of causal inferences: Sensitivity analysis for pragmatic social science” (Frank et al., 2023, Social Science Research).
Read Full Article →
Video Introductions to Sensitivity Analysis
Watch our quick video overviews covering the foundational concepts and theoretical frameworks behind sensitivity analysis.
Browse All Videos →Introduction to Sensitivity Analysis * KonFound-It! * September 2025
Introduction to Two Frameworks for Sensitivity Analysis * KonFound-It! * October 2025
Meet the Team
We are a group of researchers spanning numerous institutions who would like to contribute to better communications of research inferences and findings.
Current team members include:
- Ken Frank, Michigan State University
- Spiro Maroulis, University of Kentucky
- Qinyun Lin, University of Gothenburg
- Ran Xu, University of Connecticut
- Joshua Rosenberg, University of Tennessee, Knoxville
- Guan Saw, Claremont Graduate University
- Bret Staudt Willet, Florida State University
- Xuesen Cheng, Michigan State University
- Jihoon Choi, Michigan State University
- Gaofei Zhang, University of Connecticut
Additional contributors include:
- Tingqiao Chen (Michigan State University)
- Zixi Chen, NYU Shanghai
- Yunhe Cui, University of Connecticut
- Tenglong Li, Xi’an Jiaotong-Liverpool University
- Yuqing Liu, Michigan State University
- Dallin Overstreet, Arizona State University
- Wei Pan, Duke University
- Wei Wang, University of Tennessee, Knoxville
Tools
KonFound-It! Shiny App
Run sensitivity analyses directly in your web browser without installing any software. Built on the konfound R package.
Launch Shiny App →Rosenberg, J. M., Narvaiz, S., Xu, R., Lin, Q., Maroulis, S., Frank, K. A., Saw, G., & Staudt Willet, K. B. (2024). Konfound-It!: Quantify the robustness of causal inferences [R Shiny app built on konfound R package version 1.0.3]. https://konfound-project.shinyapps.io/konfound-it/
WWC Benchmarks
Explore sensitivity analyses calculated specifically for the What Works Clearinghouse (WWC) benchmark dataset.
Explore WWC Benchmarks →Maroulis, S., Overstreet, D., Frank, K. A., & Staudt Willet, K. B. (2024). What works clearinghouse Sensitivity analysis benchmarks. https://konfound-project.shinyapps.io/wwc-sensitivity-benchmark/
Statistical Packages
KonFound R Package
Quantify the robustness of causal inferences in R.
Rosenberg, J. M., Xu, R., Lin, Q., Maroulis, S., & Frank, K. A. (2025). konfound: Quantify the robustness of causal inferences (v. 1.0.3). https://CRAN.R-project.org/package=konfound
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
Download Metrics
| Monthly Downloads | Total Downloads |
|---|---|
Installation & Usage
install.packages("konfound")
library(konfound)
pkonfound(est_eff = -9.01, std_err = .68, n_obs = 7639, n_covariates = 221)
- Development version of the R package: KonFound Project on GitHub
KonFound Stata Package
Command to quantify robustness of causal inferences in Stata.
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. https://doi.org/10.1177/1536867X19874223
Publication Metrics (May 2026)
- Total Views & Downloads: 8,165
- Web of Science Citations: 174
- Crossref Citations: 188
Download Metrics
| Monthly Downloads | Total Downloads (Jan. 2026) |
|---|---|
| 412/month | 18,023 |
Installation & Usage
ssc install konfound
ssc install indeplist
ssc install moss
ssc install matsort
pkonfound -9.01 .68 7639 221
Resources & Documentation
Interal Resources
Explore our documentation, guides, and project updates:
External Resources
We refer to a lot of open resources for building this site, including:
Who to Contact
Have a specific question about an application, package, or potential collaboration? Reach out to the team lead for your area:
| Inquiry Topic | Primary Contact | Institutional Affiliation |
|---|---|---|
| Overall Project Inquiries | Ken Frank | Michigan State University |
| WWC Benchmarks | Spiro Maroulis | University of Kentucky |
| R Package Maintenance | Qinyun Lin | University of Gothenburg |
| R Shiny Web App | Joshua Rosenberg | University of Tennessee, Knoxville |
| Stata Package Maintenance | Ran Xu | University of Connecticut |
| Practical Guide | Guan Saw | Claremont Graduate University |
| Website & Infrastructure | Bret Staudt Willet | Florida State University |
| R Package Bug Reports | GitHub Issues | KonFound-It! GitHub |
| General Q&A & Support | Google Group Forum | Community Forum |
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