Causal Inference with Bipartite Designs

TL;DR

This paper introduces methods for unbiased causal inference in bipartite experiments, where treatments and outcomes are measured on different sets of units, addressing interference effects and improving estimation accuracy.

Contribution

It applies generalized propensity score techniques to bipartite designs, ensuring unbiased causal effect estimates and proper confidence set coverage under standard assumptions.

Findings

Significant bias reduction in causal effect estimates

Improved confidence set coverage in simulations

Effective handling of interference in bipartite experiments

Abstract

Bipartite experiments are a recent object of study in causal inference, whereby treatment is applied to one set of units and outcomes of interest are measured on a different set of units. These experiments are particularly useful in settings where strong interference effects occur between units of a bipartite graph. In market experiments for example, assigning treatment at the seller-level and measuring outcomes at the buyer-level (or vice-versa) may lead to causal models that better account for the interference that naturally occurs between buyers and sellers. While bipartite experiments have been shown to improve the estimation of causal effects in certain settings, the analysis must be done carefully so as to not introduce unnecessary bias. We leverage the generalized propensity score literature to show that we can obtain unbiased estimates of causal effects for bipartite experiments under a standard set of assumptions. We also discuss the construction of confidence sets with proper coverage probabilities. We evaluate these methods using a bipartite graph from a publicly available dataset studied in previous work on bipartite experiments, showing through simulations a significant bias reduction and improved coverage.