Network Synthetic Interventions: A Causal Framework for Panel Data Under Network Interference

TL;DR

This paper introduces Network Synthetic Interventions (NSI), a causal inference method for panel data with network interference, leveraging a novel latent factor model to estimate counterfactual outcomes accurately.

Contribution

It develops a new estimator, NSI, that accounts for network spillovers and unobserved confounding, extending synthetic control methods to network settings.

Findings

NSI consistently estimates mean outcomes under counterfactual treatments.

The estimator is asymptotically normal.

Validity tests and a graph-based experiment design ensure reliable counterfactual estimates.

Abstract

We propose a generalization of the synthetic controls and synthetic interventions methodology to incorporate network interference. We consider the estimation of unit-specific potential outcomes from panel data in the presence of spillover across units and unobserved confounding. Key to our approach is a novel latent factor model that takes into account network interference and generalizes the factor models typically used in panel data settings. We propose an estimator, Network Synthetic Interventions (NSI), and show that it consistently estimates the mean outcomes for a unit under an arbitrary set of counterfactual treatments for the network. We further establish that the estimator is asymptotically normal. We furnish two validity tests for whether the NSI estimator reliably generalizes to produce accurate counterfactual estimates. We provide a novel graph-based experiment design that guarantees the NSI estimator produces accurate counterfactual estimates, and also analyze the sample complexity of the proposed design. We conclude with simulations that corroborate our theoretical findings.