TL;DR
This paper introduces Inspection-Guided Randomization (IGR), a flexible and transparent framework for restricted randomization that improves causal effect estimation by filtering undesirable assignments based on domain-specific criteria, enhancing reproducibility and robustness.
Contribution
The paper presents IGR, a novel, transparent, and flexible restricted randomization method applicable beyond covariate balance, with pre-registered assignments to prevent p-hacking and improve experimental validity.
Findings
IGR improves effect estimates in simulated education and behavioral health experiments.
IGR effectively filters undesirable treatment assignments based on domain-informed criteria.
Simulation results show IGR outperforms benchmark designs in various experimental settings.
Abstract
Randomized experiments are considered the gold standard for estimating causal effects. However, out of the set of possible randomized assignments, some may be 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…
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