Striking the Right Balance: Why Standard Balance Tests Over-Reject the Null, and How to Fix It - Jason Kerwin; Nada Rostom; Olivier Sterck (Institute of Labor Economics (IZA), 2024)
Economists often use balance tests to demonstrate that the treatment and control groups are comparable prior to an intervention. We show that typical implementations of balance tests have poor statistical properties. Pairwise t-tests leave it unclear how many rejections indicate overall imbalance. Omnibus tests of joint orthogonality, in which the treatment is regressed on all the baseline covariates, address this ambiguity but substantially over-reject the null hypothesis using the sampling-based p-values that are typical in the literature. This problem is exacerbated when the number of covariates is high compared to the number of observations. We examine the performance of alternative tests, and show that omnibus F-tests of joint orthogonality with randomization inference p-values have the correct size and reasonable power. We apply these tests to data from two prominent recent articles, where standard F-tests indicate imbalance, and show that the study arms are actually balanced when appropriate tests are used.
Keywords: C1; C9; O12; 330; balance tests; power; size; randomization inference