Conditional randomization tests of causal effects with interference between units

TL;DR

This paper introduces a new framework for conducting valid and powerful randomization tests to assess causal effects in the presence of interference between units, applicable to complex experimental designs.

Contribution

It formalizes the concept of a conditioning mechanism for interference and demonstrates its effectiveness in two-stage randomized designs, improving over existing methods.

Findings

Enhanced statistical power in tests for interference effects.

Applicable to complex, real-world randomized experiments.

Improved computational efficiency over previous approaches.

Abstract

Many causal questions involve interactions between units, also known as interference, for example between individuals in households, students in schools, or firms in markets. In this paper, we formalize the concept of a conditioning mechanism, which provides a framework for constructing valid and powerful randomization tests under general forms of interference. We describe our framework in the context of two-stage randomized designs and apply our approach to a randomized evaluation of an intervention targeting student absenteeism in the School District of Philadelphia. We show improvements over existing methods in terms of computational and statistical power.