# Designs for estimating the treatment effect in networks with interference

## TL;DR

This paper proposes new experimental designs for estimating causal effects in networked settings with interference, using a graph coloring approach to improve estimator properties.

## Contribution

It introduces a novel quasi-coloring design inspired by matching, enhancing causal inference in networks with interference and homophily.

## Key findings

- Classical Neymanian estimator performs well with the new designs.
- Designs are easily implementable and effective in various interference scenarios.
- The approach extends to networks with homophily.

## Abstract

In this paper we introduce new, easily implementable designs for drawing causal inference from randomized experiments on networks with interference. Inspired by the idea of matching in observational studies, we introduce the notion of considering a treatment assignment as a quasi-coloring" on a graph. Our idea of a perfect quasi-coloring strives to match every treated unit on a given network with a distinct control unit that has identical number of treated and control neighbors. For a wide range of interference functions encountered in applications, we show both by theory and simulations that the classical Neymanian estimator for the direct effect has desirable properties for our designs. This further extends to settings where homophily is present in addition to interference.