Causal inference with bipartite designs: A generalized propensity score approach

Understand

Bipartite experiments, in which one set of units receives a treatment while outcomes are measured on another set, have recently garnered attention for their ability to capture interference across two distinct populations, such as buyers and sellers in online marketplaces.

• However, analyzing these experiments can be challenging, given that exposure is neither purely binary nor independent across units.
• In this paper, we propose a unified framework for causal inference in bipartite designs that leverages generalized propensity scores (GPS) to estimate exposure-response functions.
• Under standard unconfoundedness assumptions, we show that our GPS-based estimators are unbiased and derive theoretical bounds on their variance.