Model-assisted design of experiments in the presence of network correlated outcomes
This work addresses the challenge of designing experiments with network-correlated outcomes, offering a method to enhance causal inference accuracy, though it is incremental as it builds on existing potential outcome frameworks.
The paper tackles the problem of assigning treatments in randomized experiments when outcomes are correlated via a pre-intervention network, by developing models that incorporate this correlation to optimize treatment allocation and minimize the mean square error of the estimated average treatment effect. The result shows that the proposed strategies improve on allocations ignoring network structure, as demonstrated through extensive simulations.
We consider the problem of how to assign treatment in a randomized experiment, in which the correlation among the outcomes is informed by a network available pre-intervention. Working within the potential outcome causal framework, we develop a class of models that posit such a correlation structure among the outcomes. Then we leverage these models to develop restricted randomization strategies for allocating treatment optimally, by minimizing the mean square error of the estimated average treatment effect. Analytical decompositions of the mean square error, due both to the model and to the randomization distribution, provide insights into aspects of the optimal designs. In particular, the analysis suggests new notions of balance based on specific network quantities, in addition to classical covariate balance. The resulting balanced, optimal restricted randomization strategies are still design unbiased, in situations where the model used to derive them does not hold. We illustrate how the proposed treatment allocation strategies improve on allocations that ignore the network structure, with extensive simulations.