Estimating peer effects in networks with peer encouragement designs
By Dean Eckles, René F. Kizilcec, Eytan Bakshy. 2016
134 citations recorded by the source corpus.
Subfields: Communication, Education, Sociology and Political Science
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Education
- Vittorio Orlandi
- M. Usaid Awan
- Edoardo M. Airoldi
- Daniel Lewis Sussman
- Sudeepa Roy
- Cynthia Rudin
- Alexander Volfovsky
- Marco Morucci
In Sociology and Political Science
- M. Usaid Awan
- Edoardo M. Airoldi
- Sudeepa Roy
- M. Zhang
- Cynthia Rudin
- Andrew Tomkins
- Alexander Volfovsky
- Marco Morucci
Citations in the corpus
- Reviewer bias in single- versus double-blind peer review (2017, background): “(22) in a similar situation, providing sufficient statistics for analysis.”
- Elements of estimation theory for causal effects in the presence of network interference (2017, background): “…of interference into account include block and neighbor designs [David and Kempton, 1996], graph cluster design [Ugander et al., 2013], nomination designs, high-degree designs [Kim et al., 2015], peer encouragement designs [Eckles et al., 2016] and model-based designs [Basse and Airoldi, 2015].”
- Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference (2020, background): “Some of this work makes explicit use of observed network structure to randomly assign treatment so as to reduce interference (Ugander et al., 2013; Toulis and Kao, 2013; Eckles et al., 2016, 2017; Jagadeesan et al., 2019).”