TL;DR —
Unlock the potential of causal clustering with practical recommendations from Davide Viviano and team. Understand how to choose the optimal adjacency matrix based on prior knowledge, decide between cluster or Bernoulli design with a rule of thumb, and implement optimal clusters using a straightforward algorithm. A comprehensive guide for researchers aiming for precision in their experimental designs.
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
Empirical illustration and numerical studies
A Notation
This paper is available on arxiv under CC 1.0 license.
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Topics and
tags
tags
experimental-design|cluster-designs|network-design|cluster-experiments|network-experiments|causal-clustering|optimal-cluster-design|network-data-analysis
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