Estimating Total Effects in Bipartite Experiments with Spillovers and Partial Eligibility
This addresses the challenge of accurate causal inference in real-world bipartite systems with partial eligibility and spillovers, offering a practical solution for researchers and practitioners, though it is incremental in extending existing interference-aware methods.
The paper tackles the problem of estimating treatment effects in bipartite experiments where only some units are eligible for treatment but all units interact, causing interference, and develops estimators that recover primary and secondary total effects with low bias and variance in simulations and correct interference bias in field experiments.
We study randomized experiments in bipartite systems where only a subset of treatment-side units are eligible for assignment while all units continue to interact, generating interference. We formalize eligibility-constrained bipartite experiments and define estimands aligned with full deployment: the Primary Total Treatment Effect (PTTE) on eligible units and the Secondary Total Treatment Effect (STTE) on ineligible units. Under randomization within the eligible set, we give identification conditions and develop interference-aware ensemble estimators that combine exposure mappings, generalized propensity scores, and flexible machine learning. We further introduce a projection that links treatment- and outcome-level estimands; this mapping is exact under a Linear Additive Edges condition and enables estimation on the (typically much smaller) treatment side with deterministic aggregation to outcomes. In simulations with known ground truth across realistic exposure regimes, the proposed estimators recover PTTE and STTE with low bias and variance and reduce the bias that could arise when interference is ignored. Two field experiments illustrate practical relevance: our method corrects the direction of expected interference bias for a pre-specified metric in both studies and reverses the sign and significance of the primary decision metric in one case.