Inspection-Guided Randomization: A Flexible and Transparent Restricted Randomization Framework for Better Experimental Design

Abstract

Randomized experiments are considered the gold standard for estimating causal effects. However, out of the set of possible randomized assignments, some may be more likely to produce poor effect estimates and misleading conclusions. Restricted randomization is an experimental design strategy that filters out undesirable treatment assignments, but its application has primarily been limited to ensuring covariate balance in two-arm studies where the target estimand is the average treatment effect. Other experimental settings with different design desiderata and target effect estimands could also stand to benefit from a restricted randomization approach. We introduce inspection-guided randomization (IGR), a transparent and flexible framework for restricted randomization that filters out undesirable treatment assignments by inspecting assignments against analyst-specified, domain-informed design desiderata. In IGR, the acceptable treatment assignments are locked in ex ante and preregistered in the trial protocol, thus safeguarding against p-hacking and promoting reproducibility. Through illustrative simulation studies motivated by behavioral health and education interventions, we demonstrate how IGR can improve effect estimates compared to benchmark designs in experiments involving interference and group formation.