Causal Identification in Organizational Research Summer School
Causal identification is critical to the testing of theoretical relationships in organizational science. This course is designed to provide an understanding of methodological concepts as well as skill in apply various analytical techniques for causal identification. As such, the main objective of this course is for students to develop the research skills related to causal inference and identification.
Session 1: Causality and the Potential Outcomes Framework
Session 2: Treatment Effects
Session 3: Endogeneity
Session 4: Sample Selection
Session 5: Panel Data Methods
Session 6: Matching Models
Session 7: Difference-in-Differences Estimators and Regression Discontinuity Design
Session 8: Discussion of research projects
This course requires students to write a short empirical paper on a research topic of their choice. This paper should apply at least some of the methods in the course to an empirical problem. It should be 5-15 pages and focus on the research design, data, methodology, results, and analysis. Co-authored projects are encouraged.
Bettis, R. A. 2012. The search for asterisks: Compromised statistical tests and flawed theories. Strategic Management Journal, 33: 108-113.
Goldfarb, B., & King, A. A. 2016. Scientific apophenia in strategic management research: Significance tests & mistaken inference. Strategic Management Journal, 37: 167-176.
Holland, P. W. 1986. Statistics and causal inference. Journal of the American Statistical Association, 81: 945-960.
Shaver, J. M. In press. Causal identification through a cumulative body of research in the study of strategy and organizations. Journal of Management: 0149206319846272.
Cameron & Trivedi, Chapter 25, “Treatment Evaluation”
Imbens, G. 2004. Nonparametric Estimation of Average Treatment Effects under Exogeneity: A Review. Review of Economics and Statistics, 86(1): 4-29.
Meyer, B. D. 1995. Natural and quasi-experiments in economics. Journal of Business & Economic Statistics, 13: 151-161.
Rubin, D.B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of educational Psychology, 66(5):688-701.
Angrist, J.D. & Krueger, A.B., 2001. Instrumental variables and the search for identification: From supply and demand to natural experiments. Journal of Economic perspectives, 15(4): 69-85.
Hamilton, B. H., & Nickerson, J. A. 2003. Correcting for endogeneity in strategic management research. Strategic Organization, 1: 51-78.
Bascle, G. 2008. Controlling for endogeneity with instrumental variables in strategic management research. Strategic Organization, 6: 285-327.
Semadeni, M., Withers, M. C., & Certo, S. T. 2014. The perils of endogeneity and instrumental variables in strategy research: Understanding through simulations. Strategic Management Journal, 35: 1070-1079.
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: 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: 286-347.
Lennox, C. S., Francis, J. R., & Wang, Z. 2012. Selection models in accounting research. Accounting Review, 87: 589-616.
Rubin, Donald B. 2008. For Objective Causal Inference, Design Trumps Analysis. Annals of Applied Statistics, 2(3): 808-840.
Greve, H. R., & Goldeng, E. 2004. Longitudinal analysis in strategic management. In D.J. Ketchen, Jr. & D. D. Bergh (Eds.), Research Methodology in Strategy and Management.
Bowen, H. P., & Wiersema, M. F. 1999. Matching method to paradigm in strategy research: Limitations of cross-sectional analysis and some methodological alternatives. Strategic Management Journal, 20: 625-636.
Certo, S. T., & Semadeni, M. 2006. Strategy research and panel data: Evidence and implications. Journal of Management, 23: 449-471.
Certo, S. T., Withers, M. C., & Semadeni, M. 2017. A tale of two effects: Using longitudinal data to compare within- and between-firm effects. Strategic Management Journal, 38: 1536-1556.
Rubin, D. B. 2001. Using propensity scores to help design observational studies: Application to the tobacco litigation. Health Services and Outcomes Research Methodology, 2: 169-188.
Stuart, E. A. 2010. Matching methods for causal inference: A review and a look forward. Statistical Science: A Review Journal of the Institute of Mathematical Statistics, 25: 1-21.
Li, M. 2012. Using the propensity score method to estimate causal effects: A review and practical guide. Organizational Research Methods, 16: 188-226.
Angrist & Pischke, Chapter 5.2, “Difference-in-differences”
Angrist & Pischke, Chapter 6, “Getting a Little Jumpy: Regression Discontinuity Designs”
Bertrand, M, Dufloo, E., & Mullainathan, S. 2004. How Much Should We Trust Differences-in-Differences Estimates? Quarterly Journal of Economics, 119(1): 249-275.
Flammer, C., & Bansal, P. 2016. Does a long-term orientation create value? Evidence from a regression discontinuity. Strategic Management Journal, 38: 1827-1847.
Imbens, G. W. & Lemieux, T. 2008. Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142: 615-635.
The timetable for this course can be found here.
External (non-ERIM) participants are welcome to this course. To register, please fill in the registration form and e-mail it to the ERIM Doctoral Office by four weeks prior to the start of the course. For external participants, the course fee is 750 euro.
The registration deadline is 22 May 2020. Please note that the number of places for this course is limited. In case the number of registrations exceeds the number of available seats, priority is given to ERIM RM students and PhD candidates.