Design Strategies for Networked Experiments via Interference Balancing

Randomized experiments are widely used to estimate the causal effects of treatment or intervention across various scientific fields. Nowadays, numerous experiments are conducted in network settings where the outcome of one unit depends not only on its own treatment but also on the treatments of other units within the network. This phenomenon, known as network interference, poses significant challenges for experimental design. Existing randomization methods often neglect interference or rely on strict assumptions, such as homogeneous peers or linearity in peer interference. We introduce two innovative and easy-to-implement randomization designs for estimating direct treatment effects in the presence of heterogeneous or nonlinear network interference: network interference balancing rerandomization (NetRR) and network maximized matching randomization (NetMM). We explore the theoretical properties of two widely used estimators under these proposed randomization designs. Numerical results in the simulated experiment and the real-world social network experiment confirm the desirable performance of the proposed methods. Additionally, we discuss the trade-off between the risks associated with model assumptions and the feasibility of the randomization design.