Causal Clusterings in the Presence of Endogenous Peer Effects

TLDR

The paper investigates the optimal design of clustering in experimental setups, particularly in the context of social networks. It discusses theoretical frameworks, objective functions, and practical algorithms for choosing the best clustering method. The authors analyze the impact of various factors, including bias, variance, and spillover effects, providing recommendations for real-world applications.via the TL;DR App

Authors:

(1) Davide Viviano, Department of Economics, Harvard University;

(2) Lihua Lei, Graduate School of Business, Stanford University;

(3) Guido Imbens, Graduate School of Business and Department of Economics, Stanford University;

(4) Brian Karrer, FAIR, Meta;

(5) Okke Schrijvers, Meta Central Applied Science;

(6) Liang Shi, Meta Central Applied Science.

Table of Links

Abstract & Introduction

Setup

(When) should you cluster?

Choosing the cluster design

Empirical illustration and numerical studies

Recommendations for practice

References

A) Notation

B) Endogenous peer effects

C) Proofs

B Endogenous peer effects

This paper is available on arxiv under CC 1.0 license.

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Published by HackerNoon on 2024/01/31