Why Randomized HR Evaluations May Mislead Managers: The Role of Treatment–Trait Interactions in Outcome Construction

This paper examines whether randomized evaluations can fail to identify causal effects when outcomes include interactions between treatment and unobserved characteristics. We show that even under random assignment, standard regression estimators do not necessarily recover the structural causal effect if outcomes contain non-separable interaction terms between treatment and latent characteristics. When outcomes contain such non-separable interaction terms, the estimated treatment effect reflects interaction components embedded in the outcome construction and may fail to recover the structural policy parameter. We derive conditions under which unbiased identification is restored, highlighting the critical role of additive separability. The results provide a theoretical foundation for understanding when randomized evaluations may yield misleading conclusions in managerial and policy contexts.