Program

Day 1: June 11, 2018

Location: Chateauform Republique

Room: EM-CR Ray Charles

9:30-9:55 Introductions

Statistical/econometric models for networks

• 9:55-10:30 Michael Leung: Dependence-robust inference using resampling-based statistics
• 10:35-11:10 Luis Candelaria: A Semiparametric Network Formation Model with Multiple Linear Fixed Effects
• 11:15-11:50 Eric Auerbach: Identification and Estimation of a Partially Linear Regression Model using Network Data
• 11:55-12:30 Ilya Shpitser: Identification and estimation of causal effects from dependent data

12:30-1:30 Discussion

Day 2: June 12, 2018

Location: University of Chicago Center

6 Rue Thomas Mann, 75013 Paris

9:30-9:45 Introductions and recap of day 1

Statistical models for networks revisited

• 9:45-10:20 Kenneth Frank: The importance of Model Specification for Causal Inference in Network Analysis
• 10:25-11:00 Karl Rohe: Inferring a social graph's spectral properties from a link tracing sample to monitor HIV prevalence
• 11:05-11:20 Elizabeth Ogburn: Replication Crisis
• 11:25-11:40 Daniel Sussman: TBA

11:40-1:00 Lunch (at the venue with discussion)

Interference

• 1:00-1:35 David Choi: Using Exposure Mappings as Side Information in Network Experiments
• 1:40-2:15 Forrest Crawford: Causal inference under contagion: randomization and marginal effects
• 2:20-2:55 Laura Forastiere: Estimating Causal Effects Under Interference Using Bayesian Generalized Propensity Score

Experiments in networks

• 3:00-3:35 Hyunseung Kang: Spillover Effects in Clustered Randomized Trials with Noncompliance
• 3:40-4:15 Dean Eckles: Automated randomization for networks stuff
• 4:20-4:35 Alexander Volfovsky: Causal inference on networks: from randomization to observational studies

4:35-... Discussion and wrap up