A plain-language guide, with a worked R example Posted: 2026-07-29 This is based on: Huang, F. L. (2026). Using fidelity of implementation data in randomized controlled trials: A primer on using instrumental variables in educational research. The Journal of Experimental Education. https://www.tandfonline.com/doi/full/10.1080/00220973.2026.2672864
Jul 28, 2026
Statistics students are taught that correlation does not equal causation. Just because two variables (e.g., x and y) are related to each other does not necessarily mean that one causes the other (e.g., x causes y). The correlation coefficients (i.e., ρ) for ρ(x, y) and ρ(y, x) are the same and does not provide information on the directionality of the effect (e.g., x → y or x ← y). It could also be that the variables are related due to a third variable z which causes both (i.e., a confounder).
May 26, 2025
Earlier this year, I wrote an article on using instrumental variables (IV) to analyze data from randomized experiments with imperfect compliance (read the manuscript for full details; link updated; it’s open access). In the article, I described the steps of IV estimation and the logic behind it.
Aug 17, 2018
A primer on using IVs
Jun 27, 2018