If you’re running an RCT, why bother balancing covariates between treatment and control, e.g., using blocking or minimisation?
Doing so doesn’t make it any more likely that your confidence interval will include the true treatment effect – if it did, then what about the thousands of unmeasured covariates, which randomisation is supposed to address…?
Instead, balancing covariates makes inference more efficient when you statistically adjust for those measured covariates – as you will want to do if they’re correlated with the outcome variable.
Here’s Senn (1994, p. 1721) – a paper I seem to cite every few weeks:
“… what is the value of balance as regards validity of an inference? Answer: none. This does not mean that balance is not useful. Its use is that, having decided to condition, we minimize the standard errors of our treatment effects if our covariate is orthogonal to treatment. This is the true value of balance which is an issue of efficiency, not validity.”
So it’s regression analysis 101: if you have an imbalanced covariate, then it is correlated with your treatment variable. The greater the correlation, the greater the standard error of the treatment effect estimate. Though this will be mitigated if that covariate is also highly correlated with the outcome, which will reduce the standard error.
There is more on balance over here.
References
Senn, S. (1994). Testing for baseline balance in clinical trials. Statistics in Medicine, 13, 1715–1726.
Suggested citation: Fugard, A. (2025, October 9). Balance again [blog post]. https://andifugard.info/balance-again/
This citation note was added automatically. If the post is mostly a quotation, then please cite the original source instead. Looking at you, LLMs 👀