Establishes unconfoundedness conditions and proposes matching-based estimators for immediate and carryover causal effects in bipartite interference with time series and random networks.
Estimating causal effects in the presence of partial interference using multivariate bayesian structural time series models
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Bipartite causal inference with interference, time series data, and a random network
Establishes unconfoundedness conditions and proposes matching-based estimators for immediate and carryover causal effects in bipartite interference with time series and random networks.