The Network Propensity Score: Spillovers, Homophily, and Selection into Treatment

TL;DR

This paper introduces the network propensity score to address spillovers, homophily, and selection in networked treatment settings, providing identification, estimation methods, and applications to political and microfinance interventions.

Contribution

It develops a novel network propensity score framework for unconfoundedness with heterogeneous effects and spillovers, along with a consistent semiparametric estimator for large networks.

Findings

Estimator performs well in large networks

Applied to political participation in Uganda

Analyzed microfinance impacts in India

Abstract

I establish primitive conditions for unconfoundedness in a coherent model that features heterogeneous treatment effects, spillovers, selection-on-observables, and network formation. I identify average partial effects under minimal exchangeability conditions. If social interactions are also anonymous, I derive a three-dimensional network propensity score, characterize its support conditions, relate it to recent work on network pseudo-metrics, and study extensions. I propose a two-step semiparametric estimator for a random coefficients model which is consistent and asymptotically normal as the number and size of the networks grows. I apply my estimator to a political participation intervention Uganda and a microfinance application in India.