About KonFound-It!

Learn about the project and meet the team

Welcome to our space about sensitivity analysis.

Questions? Issues? Suggestions? Reach out through the KounFound-It! Google Group.

Purpose

All of the assumptions of statistical analysis rarely hold. So the challenge for the pragmatist is to understand when evidence is strong enough to support action. That’s where sensitivity analysis comes in—so we can understand how robust our inferences are to challenges to our assumptions. One example is a statement such as:

XX% of the estimated effect would have to be due to bias to change your inference about the effect.

But that’s just one. In this website, we discuss lots of approaches, applications, and the pluses and minuses.

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.

Learn More About Sensitivity Analysis

Listen to an AI-generated podcast, created by Google’s NotebookLM, discussing the KonFound-It! team’s article “Quantifying the robustness of causal inferences: Sensitivity analysis for pragmatic social science” in Social Science Research (Frank, Lin, Xu, Maroulis, & Mueller, 2023):

People

We are a group of researchers spanning numerous institutions who would like to contribute to better communications of research inferences and findings.

Our group includes:

Additional contributors include:

  • Tingqiao Chen (Michigan State University)
  • Zixi Chen (NYU Shanghai)
  • Xuesen Cheng (Michigan State University)
  • Jihoon Choi (Michigan State University)
  • Tenglong Li (Xi’an Jiaotong-Liverpool University)
  • Yuqing Liu (Michigan State University)
  • Dallin Overstreet (Arizona State University)
  • Wei Pan (Duke University)
  • Wei Wang (Michigan State University)
  • Gaofei Zhang (University of Connecticut)

Tools

KonFound-It! 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 1.0.2]. https://konfound-project.shinyapps.io/konfound-it/

Benchmarks: What Works Clearinghouse

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/

KonFound R Package

Rosenberg, J. M., Xu, R., Lin, Q., Maroulis, S., & Frank, K. A. (2023). konfound: Quantify the robustness of causal inferences (v. 0.4.0). https://CRAN.R-project.org/package=konfound

Monthly Downloads Total Downloads

Line plot of konfound package downloads over time

To install:

  • Development version of the R package: KonFound Project on GitHub
  • 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
install.packages("konfound")
library(konfound)
pkonfound(est_eff = -9.01, std_err = .68, n_obs = 7639, n_covariates = 221)

KonFound Stata Package

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

Total Downloads (Dec. 2023)
9,969

To install:

ssc install konfound
ssc install indeplist
ssc install moss
ssc install matsort
pkonfound -9.01 .68 7639 221

Internal Resources

Be sure to look through the variety of supports for KonFound:

External Resources

We refer to a lot of open resources for building this site, including:

Connect

Thanks for visiting! Happy KonFounding!