Estimating Total Effects in Bipartite Experiments with Spillovers and Partial Eligibility

TL;DR

This paper develops methods for estimating total effects in bipartite experiments with interference and partial eligibility, enabling accurate effect estimation and bias correction in complex experimental settings.

Contribution

It introduces a formal framework for eligibility-constrained bipartite experiments, along with interference-aware estimators that incorporate exposure mappings, propensity scores, and machine learning.

Findings

Estimators accurately recover total effects with low bias and variance.

Method corrects interference bias in real experiments.

Estimates can reverse expected bias directions in practical cases.

Abstract

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.