A1623
Title: Randomization tests in bipartite experiments
Authors: Jizhou Liu - Peking University (China) [presenting]
Sizhu Lu - University of California Berkeley (United States)
Peng Ding - University of California, Berkeley (United States)
Abstract: Randomized experiments with bipartite structures are increasingly used to study interventions where treatments and outcomes are defined on distinct but interdependent populations. A framework for Fisherian-style randomization tests tailored for bipartite experiments is developed that formalizes randomization tests on a subset of units over a restricted assignment space defined by the bipartite structure. The proposed procedures are finite-sample valid under commonly used experimental designs, including complete randomization and Bernoulli trials, and extend naturally to more general designs through conditional sampling. General network experiments can be represented within the bipartite framework, enabling the analysis of total and spillover effects on networks. Simulation and empirical studies illustrate the validity and practical relevance of this approach.
