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Activity Number: 207 - Experiments and Inference for Social Networks
Type: Invited
Date/Time: Monday, July 31, 2017 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract #321889 View Presentation
Title: Massive Meta-Analysis with Experiments as Instruments: Applications to Peer Effects in Networks
Author(s): Dean Eckles* and Alexander Peysakhovich
Companies: MIT and Facebook
Keywords: causal inference ; social networks ; instrumental variables ; regularization ; Facebook ; randomized experiments
Abstract:

The widespread adoption of randomized experiments (i.e. A/B tests) in the Internet industry means that there are often numerous well-powered experiments on a given product. Individual experiments are often simple "bake-off" evaluations of a new intervention: They allow us to estimate effects of that particular intervention on outcomes of interest, but they are often not informative about the mechanisms for these effects or what other inventions might do. We consider what else we can learn from a large set of experiments. In particular, we use many experiments to learn about the effects of the various endogenous variables (or mechanisms) via which the experiments affect outcomes. This involves treating the experiments as instrumental variables, and so this setting is similar to, but somewhat different from, "many instrument" settings in econometrics and biostatistics. Motivated by the distribution of experiment first-stage effects, we present and evaluate sparsity-inducing regularization methods and cross-validation for instrumental variables. Our applications are to estimating peer effects in online social networks mediated by ranking systems.


Authors who are presenting talks have a * after their name.

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