For any study that makes — or critiques — a causal claim. Curated from the leadership-research methodology literature, where causal-identification standards are most fully developed. The principles transfer to HRD, teams, and organizational research broadly.
The core problem
Most organizational research is observational, so apparent effects may reflect endogeneity — omitted variables, selection bias, or reverse causality — rather than true cause and effect. These methods address that.
Causal identification strategies
- Natural experiments — Sieweke (2020); systematic review + quality criteria for leadership settings.
- Difference-in-differences (DiD) — Lee et al. (2025); DiD with matching; four validity conditions + decision tree.
- Instrumental variables (IV) — Bastardoz et al. (2023); 77-study review; three instrument conditions; common pitfalls.
- Regression discontinuity, exogenous shocks — Jacquart et al. (2024); three-dimensional shock taxonomy.
- Propensity score methods — Narita, Tena, & Detotto (2023); weighting tutorial.
- Directed acyclic graphs (DAGs) — Hünermund et al. (2025); choosing good vs. bad vs. unnecessary controls.
Measurement validity (where causal claims quietly fail)
- Banks, Woznyj, & Mansfield (2023) — only ~3% of leadership variables measure actual behavior.
- Fischer, Dietz, & Antonakis (2024) — positive-leadership constructs as “causal illusions.”
- Fischer et al. (2023) — the ABC Framework; behavioral counterfactuals as a validity criterion.
Open science, rigor & replication
- Antonakis (2017) — the landmark reform editorial (the “five diseases”); registered reports + null results.
- Wulff et al. (2023) — catalog of 20+ common methodological mistakes.
- Aguinis, Li, & Foo (2024) — Research Transparency Index (RTI).
- Gerpott, Briker, & Banks (2024) — registered-report formats.
Theory that supports causal work
- Dietz (2026) — actionable-theory typology (Manipulate / Select / Observe); building manipulable causes.
- Kuljanin et al. (2024) — computational process theories (formalizing mechanisms as code).
Effect sizes & benchmarks
- Amari et al. (2025) — correlational benchmarks are invalid for causal effects; a benchmarking blueprint.
How to use this page
Use it as a design-stage checklist before collecting data, and as a critique lens when reviewing others’ causal claims in your literature review.
Source: Leadership-WIKI “Causal Inference & Methodology” cluster (66 pages).
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References
Aguinis, H., Li, Z. A., & Foo, M. D. (2024). The research transparency index. The Leadership Quarterly, 35(4), 101809. https://doi.org/10.1016/j.leaqua.2024.101809
Amari, P., Banks, G. C., Bourque, L., Holladay, H., & O’Boyle, E. (2025). Effect size benchmarks: Time for a causal renaissance. The Leadership Quarterly, 36, 101855.
Antonakis, J. (2017). On doing better science: From thrill of discovery to policy implications. The Leadership Quarterly, 28(1), 5–21.
Banks, G. C., Woznyj, H. M., & Mansfield, C. A. (2023). Where is “behavior” in organizational behavior? A call for a revolution in leadership research and beyond. The Leadership Quarterly, 34, 101581.
Bastardoz, N., Matthews, M. J., Sajons, G. B., Ransom, T., Kelemen, T. K., & Matthews, S. H. (2023). Instrumental variables estimation: Assumptions, pitfalls, and guidelines in leadership research. The Leadership Quarterly, 34, 101673.
Dietz, J. (2026). Building actionable theories: The role of causal constructs. The Leadership Quarterly, 37, 101929.
Fischer, T., Dietz, J., & Antonakis, J. (2024). A fatal flaw: Positive leadership style research creates a causal illusion. The Leadership Quarterly, 35, 101771.
Fischer, T., Hambrick, D. C., Sajons, G. B., & Van Quaquebeke, N. (2023). Leadership science beyond questionnaires. The Leadership Quarterly, 34, 101752.
Gerpott, F. H., Briker, R., & Banks, G. C. (2024). New ways of seeing: Four ways you have not thought about registered reports yet. The Leadership Quarterly, 35, 101783.
Hünermund, P., Louw, B., & Rönkkö, M. (2025). The choice of control variables in empirical management research: How causal diagrams can inform the decision. The Leadership Quarterly, 36, 101845.
Jacquart, P., Santoni, S., Schudy, S., Sieweke, J., & Withers, M. C. (2024). Exogenous shocks: Definitions, types, and causal identification strategies. The Leadership Quarterly, 35, 101823.
Kuljanin, G., Braun, M. T., Grand, J. A., Olenick, J. D., Chao, G. T., & Kozlowski, S. W. J. (2024). Advancing organizational science with computational process theories. The Leadership Quarterly, 35.
Lee, K., Jeong, Y., Han, S., Joo, S., Park, J., & Qi, K. (2025). Difference-in-differences with matching methods in leadership research. The Leadership Quarterly, 36, 101813.
Narita, K., Tena, J. D., & Detotto, C. (2023). Causal inference with observational data: A tutorial on propensity score analysis. The Leadership Quarterly, 34, 101678. https://doi.org/10.1016/j.leaqua.2023.101678
Sieweke, J., & Santoni, S. (2020). Natural experiments in leadership research: An introduction, review, and guidelines. The Leadership Quarterly, 31, 101338. https://doi.org/10.1016/j.leaqua.2019.101338
Wulff, J. N., Sajons, G. B., Pogrebna, G., Lonati, S., Bastardoz, N., Banks, G. C., & Antonakis, J. (2023). Common methodological mistakes. The Leadership Quarterly, 34, 101677. https://doi.org/10.1016/j.leaqua.2023.101677