6 papers
Evidence aggregation with ignorance in mind: learning what we do (not) know for archetypes discovery
Emily Breza, Arun G. Chandrasekhar, Davide Viviano
When evaluating policy interventions, researchers often pursue two related goals: identifying which individuals or contexts benefit most, and determining whether patterns of treatm…
Non-robustness of diffusion estimates on networks with measurement error
Arun G. Chandrasekhar, Paul Goldsmith-Pinkham, Tyler H. McCormick +2
Network diffusion models are used to study disease transmission, information spread, technology adoption, and other socio-economic processes. We show that estimates of these diffus…
Multiplexing in Networks and Diffusion
Arun G. Chandrasekhar, Vasu Chaudhary, Benjamin Golub +1
Social and economic networks are often multiplexed, meaning that people are connected by different types of relationships -- such as borrowing goods and giving advice. We make two…
Experimenting with Networks
Arun G. Chandrasekhar, Matthew O. Jackson
We provide an overview of methods for designing and implementing experiments (field, lab, hybrid, and natural) when there are networks of interactions between subjects.
A Network Formation Model Based on Subgraphs
Arun G. Chandrasekhar, Matthew O. Jackson
We develop a new class of random graph models for the statistical estimation of network formation -- subgraph generated models (SUGMs). Various subgraphs -- e.g., links, triangles,…
General Covariance-Based Conditions for Central Limit Theorems with Dependent Triangular Arrays
Arun G. Chandrasekhar, Matthew O. Jackson, Tyler H. McCormick +1
We present a general central limit theorem with simple, easy-to-check covariance-based sufficient conditions for triangular arrays of random vectors when all variables could be int…