DeepAI AI Chat
Log In Sign Up

The Local Approach to Causal Inference under Network Interference

by   Eric Auerbach, et al.

We propose a new unified framework for causal inference when outcomes depend on how agents are linked in a social or economic network. Such network interference describes a large literature on treatment spillovers, social interactions, social learning, information diffusion, social capital formation, and more. Our approach works by first characterizing how an agent is linked in the network using the configuration of other agents and connections nearby as measured by path distance. The impact of a policy or treatment assignment is then learned by pooling outcome data across similarly configured agents. In the paper, we propose a new nonparametric modeling approach and consider two applications to causal inference. The first application is to testing policy irrelevance/no treatment effects. The second application is to estimating policy effects/treatment response. We conclude by evaluating the finite-sample properties of our estimation and inference procedures via simulation.


page 1

page 2

page 3

page 4


Causal Inference on Networks under Continuous Treatment Interference

This paper presents a methodology to draw causal inference in a non-expe...

Causal Inference through the Method of Direct Estimation

The intersection of causal inference and machine learning is a rapidly a...

Network Data

Many economic activities are embedded in networks: sets of agents and th...

Causal Network Motifs: Identifying Heterogeneous Spillover Effects in A/B Tests

Randomized experiments, or "A/B" tests, remain the gold standard for eva...

Causal Inference under Temporal and Spatial Interference

Many social events and policies generate spillover effects in both time ...

Estimating Heterogeneous Causal Effects in the Presence of Irregular Assignment Mechanisms

This paper provides a link between causal inference and machine learning...