Interactive Web App and code to implement ITCV and RIR

https://konfound-it.org

https://cran.r-project.org/web/packages/konfound/index.html

https://journals.sagepub.com/doi/pdf/10.1177/1536867X19874223

Readings on ITCV and RIR

  • Bendig, David and Jonathan Hoke (2024), “Probing for Omitted Variable Bias: The Role of the Impact Threshold of a Confounding Variable in Complementing Instrumental Variable Estimations,” Industrial Marketing Management, 122 (October),145-159.
  • Busenbark, John R., Hyunjung (Ellie) Yoon, Daniel L. Gamache, and Michael C. Withers (2022), “Omitted Variable Bias: Examining Management Research With the Impact Threshold of a Confounding Variable (ITCV),” Journal of Management, 48 (1), 17-48.
  • Frank, Kenneth A. (2000), “Impact of a confounding variable on a regression coefficient,” Sociological Methods & Research, 29 (2), 147-194.
  • Frank, Kenneth A., Qinyun Lin, Spiro Maroulis, Anna S. Mueller, Ran Xu, Joshua M. Rosenberg, Christopher S. Hayter, Ramy A. Mahmoud, Marynia Kolak, Thomas Dietz, and Lixin Zhang (2021), “Hypothetical Case Replacement can be used to Quantify the Robustness of Trial Results,” Journal of Clinical Epidemiology, 134, 150–159
  • Frank, Kenneth A., Spiro J. Maroulis, Minh Q. Duong, and Benjamin M. Kelcey (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.

Code to implement Coefficient Stability to Unobservable Selection

https://ideas.repec.org/c/boc/bocode/s457677.html

https://cran.r-project.org/web/packages/robomit/refman/robomit.html

Readings on Coefficient Stability to Unobservable Selection

  • Altonji, Joseph G., Todd E. Elder, and Christopher R. Taber (2005), “Selection on Observed and Unobserved Variables: Assessing the Effectiveness of Catholic Schools,” Journal of Political Economy, 113 (1), 151-184.
  • Oster, Emily (2019), “Unobservable Selection and Coefficient Stability: Theory and Evidence,” Journal of Business & Economic Statistics, 37 (2), 187-204.

Web App and Code to implement Copula-Based Endogeneity Correction

https://copula-correction.github.io/Webpage/index.html

https://cran.r-project.org/web/packages/Rcope/index.html

https://github.com/HashtagHaschka/Copula-based-endogeneity-corrections

Readings on Copula-Based Endogeneity Correction

  • Becker, Jan-Michael, Dorian Proksch, and Christian M. Ringle (2022), “Revisiting Gaussian Copulas to Handle Endogenous Regressors,” Journal of the Academy of Marketing Science, 50 (1), 46-66.
  • Dost, Florian and Rouven E. Haschka (2025), The Gaussian Copula Control Function Method Does Not Help Against Traditional Omitted Variable Bias. Available at SSRN: https://ssrn.com/abstract=5285127
  • Haschka, Rouven E. (2022), “Handling Endogenous Regressors using Copulas: A Generalization to Linear Panel Models with Fixed Effects and Correlated Regressors,” Journal of Marketing Research, 59(4), 860-881.
  • Park, Sungho, and Sachin Gupta (2024), “A Review of Copula Correction Methods to Address Regressor–Error Correlation,” Impact at JMR. https://www.ama.org/marketing-news/a-review-of-copula-correction-methods-to-address-regressor-error-correlation/
  • Qian, Yi, Anthony Koschmann, and Hui Xie (2026), “A Practical Guide to Endogeneity Correction Using Copulas,” Journal of Marketing, forthcoming.
  • Qian, Yi, and Xie, Hui (2024), “Correcting regressor-endogeneity bias via instrument-free joint estimation using semiparametric odds ratio models.”, Journal of Marketing Research, 61 (5), 914-936.
  • Yang, Fan, Yi Qian, and Hui Xie (2025), “Addressing Endogeneity using a Two-Stage Copula Generated Regressor Approach,” Journal of Marketing Research, 62(4), 601-623.

Marketing Readings on Endogeneity

  • Ebbes, P., Papies, D., and van Heerde, H. J. (2021), “Dealing with Endogeneity: A Nontechnical Guide for Marketing Researchers,” Handbook of Market Research, Cham: Springer, 181-217.
  • Papies, D., P. Ebbes, and H. J. van Heerde (2017), “Addressing Endogeneity in Marketing Models,” Advanced Methods for Modeling Markets, Cham: Springer, 581–627.
  • Papies, D., Ebbes, P., and Feit, E. (2024), “Endogeneity and Causal Inference in Marketing”. In: R. Winer and S. A. Neslin (Eds.), History of Marketing Science (2nd edition).
  • Rutz, O. J., & Watson, G. F. (2019), “Endogeneity and Marketing Strategy Research: An Overview,” Journal of the Academy of Marketing Science, 47, 479-498.
  • Sande, J. B., and Ghosh, M. (2018), “Endogeneity in Survey Research,” International Journal of Research in Marketing, 35(2), 185-204.

Additional Readings

  • Antonakis, J., Bendahan, S., Jacquart, P., and Lalive, R. (2010), “On Making Causal Claims: A Review and Recommendations,” The Leadership Quarterly, 21(6), 1086-1120.
  • Certo, S. T., Busenbark, J. R., Woo, H. S., & Semadeni, M. (2016), “Sample Selection Bias and Heckman Models in Strategic Management Research,” Strategic Management Journal, 37(13), 2639-2657.
  • Clougherty, J. A., Duso, T., & Muck, J. (2016), “Correcting for Self-Selection Based Endogeneity in Management Research: Review, Recommendations and Simulations,” Organizational Research Methods, 19(2), 286-347.
  • Hill, A. D., Johnson, S. G., Greco, L. M., O’Boyle, E. H., & Walter, S. L. (2021), “Endogeneity: A Review and Agenda for the Methodology-Practice Divide affecting Micro and Macro Research,” Journal of Management, 47(1), 105-143.
  • Semadeni, M., Withers, M. C., & Trevis Certo, S. (2014), “The Perils of Endogeneity and Instrumental Variables in Strategy Research: Understanding through Simulations,” Strategic Management Journal, 35(7), 1070-1079.
  • Zhang, X., Fang, H., Dou, J., & Chrisman, J. J. (2022), “Endogeneity Issues in Family Business Research: Current Status and Future Recommendations,” Family Business Review, 35(1), 91-116.