FAQs

Frequently Asked Questions About the Application of KonFound-It for Sensitivity Analysis

 Posted on January 1, 0001  |  

by Kenneth A. Frank, Qinyun Lin, Ran Xu, Spiro Maroulis, Guan Saw, & Josh Rosenberg Last updated: 7/5/2024 FAQ Dev Version Introduction This document addresses some frequently asked questions regarding KonFound. See the appendices for background readings and software. For quick reference, see: Overview of all KonFound commands Main introductory slides for combined frameworks Background There are two basic frameworks for sensitivity analysis employed within KonFound. First is the Impact Threshold for a Confounding Variable (ITCV)—Frank (2000). [Read More]

Introductory Videos

 Posted on January 1, 0001  |  

Introduction to Sensitivity Analysis * KonFound-It! * September 2025 Introduction to Two Frameworks for Sensitivity Analysis * KonFound-It! * October 2025 Calculating Impact Threshold of a Confounding Variable (ITCV) in Stata * KonFound-It! * January 2026 Quantifying Sensitivity in Causal Inference with Kenneth Frank * Statistical Horizons * March 2025 Robustness of Inference to Replacement & Fragility for Logistic Regression and Hazard Functions * Michigan State University - Epidemiology & Biostatistics * April 2024 [Read More]

Practical Guide

 Posted on January 1, 0001  |  

A Practical Guide to ITCV and RIRThe document is intended to serve as a practical guide to implementing the Impact Threshold of a Confounding Variable (ITCV), a sensitivity analysis approach based on omitted variables, and Robustness of Inference to Replacement (RIR), a sensitivity analysis approach based on replacement of cases. Download the Practical Guide (August 2025 version) → Citation *Frank, K. A., *Saw, G. K., Lin, Q., Xu, R., Rosenberg, J. [Read More]

Publications

 Posted on January 1, 0001  |  

Featured Publications Practical Guide (2025) *Frank, K. A., *Saw, G. K., Lin, Q., Xu, R., Rosenberg, J. M., Maroulis, S. J., & Staudt Willet, K. B. (2025, August). A practical guide to impact threshold of a confounding variable (ITCV) and robustness of inference to replacement (RIR) (Version 2). Michigan State University. (* Co-first authors.) White paper. Web | PDF R Package (2024) Narvaiz, S., Lin, Q., Rosenberg, J. M., Frank, K. [Read More]

Resource Overview

Tools, Explanatory Documentation, & Key Methodology Papers

 Posted on January 1, 0001  |  

Resource DirectoryAccess our suite of statistical packages, interactive web apps, explanatory slide decks, and core methodological publications. Quick Access Tools & Documentation Software & Web Apps R Package (CRAN Version) R Package (Development GitHub) KonFound-It! Shiny Web App Stata Package (SJ Article) WWC Sensitivity Benchmarks Guides & Slide Decks Frequently Asked Questions (FAQ) Overview of KonFound Techniques (PPTX) Overview of Commands & Inputs (DOCX) Quick Usage Examples Guide (PDF) Combined Frameworks Slides (PPTX) Framework Comparison Slides (PPTX) Publication Resources Empirical Examples & Calculators Published Empirical Application Examples (DOCX) — Real-world write-ups showing how to report KonFound results in peer-reviewed journals. [Read More]

Talks

Conference Presentations & Invited Seminars

 Posted on January 1, 0001  |  

Talks & PresentationsExplore academic presentations, conference talks, and slide decks from the KonFound-It! research team. Upcoming Talks More Talks Coming Soon Check back soon for upcoming conference presentations and invited academic seminars. Recent Presentations Quantifying Sensitivity to Selection on Unobserved Covariates: Recasting the Coefficient of Proportionality Joint Statistical Meetings (JSM 2025) — Multi-faceted Approaches to Sensitivity Analysis for Observational Studies Date: August 6, 2025 (10:30 AM – 12:30 PM CT) | Location: Nashville, TN [Read More]

Use Case 1

Generalized Robustness of Inference to Replacement (RIR)

 Posted on January 1, 0001  |  

How believable are the effects of a growth mindset intervention? Background “A National Experiment Reveals Where a Growth Mindset Improves Achievement” (Yeager et al., 2019) Nature, Volume 573, pages 364–369 Abstract: A global priority for the behavioural sciences is to develop cost-effective, scalable interventions that could improve the academic outcomes of adolescents at a population level, but no such interventions have so far been evaluated in a population-generalizable sample. Here we show that a short (less than one hour), online growth mindset intervention—which teaches that intellectual abilities can be developed—improved grades among lower-achieving students and increased overall enrolment to advanced mathematics courses in a nationally representative sample of students in secondary education in the United States. [Read More]

Use Case 2

Logistic Regression with a Dichotomous Outcome

 Posted on January 1, 0001  |  

How convincing is the link between cohabitation and poverty? Background “Marriage, Work, and Racial Inequalities in Poverty: Evidence from the U.S” (Thiede et al., 2017) Journal of Marriage and Family, Volume 79, pages 1241–1257 Abstract: This paper explores recent racial and ethnic inequalities in poverty, estimating the share of racial poverty differentials that can be explained by variation in family structure and workforce participation. The authors use logistic regression to estimate the association between poverty and race, family structure, and workforce participation. [Read More]

Use Case 3

Impact Threshold of a Confounding Variable (ITCV)

 Posted on January 1, 0001  |  

How robust is the link between a recent eviction and material hardship? Background “Eviction’s Fallout: Housing, Hardship, and Health” (Desmond & Kimbro, 2015) Social Forces, Volume 94, pages 295-324 Abstract: Millions of families across the United States are evicted each year. Yet, we know next to nothing about the impact eviction has on their lives. Focusing on low-income urban mothers, a population at high risk of eviction, this study is among the first to examine rigorously the consequences of involuntary displacement from housing. [Read More]

Use Case 4

Coefficient of Proportionality (CoP)

 Posted on January 1, 0001  |  

How robust is the link between low birthweight and childhood IQ when accounting for unobserved selection? Background “Unobservable Selection and Coefficient Stability: Theory and Evidence” (Oster, 2019) Journal of Business & Economic Statistics, Volume 37, Issue 2, pages 187-204 Abstract: A common approach to evaluating robustness to omitted variable bias is to observe coefficient movements after inclusion of controls. This is informative only if selection on observables is informative about selection on unobservables. [Read More]