42 citations · 67 across the 9 of their papers we have counts for
9 papers
Low-order outcomes and clustered designs: combining design and analysis for causal inference under network interference
Matthew Eichhorn, Samir Khan, Johan Ugander +1
Variance reduction for causal inference in the presence of network interference is often achieved through either outcome modeling, typically analyzed under unit-randomized Bernoull…
Analysis of Two-Stage Rollout Designs with Clustering for Causal Inference under Network Interference
Mayleen Cortez-Rodriguez, Matthew Eichhorn, Christina Lee Yu
Estimating causal effects under interference is pertinent to many real-world settings. Recent work with low-order potential outcomes models uses a rollout design to obtain unbiased…
Online Team Formation under Different Synergies
Matthew Eichhorn, Siddhartha Banerjee, David Kempe
Team formation is ubiquitous in many sectors: education, labor markets, sports, etc. A team's success depends on its members' latent types, which are not directly observable but ca…
Exploiting Neighborhood Interference with Low Order Interactions under Unit Randomized Design
Mayleen Cortez-Rodriguez, Matthew Eichhorn, Christina Lee Yu
Network interference, where the outcome of an individual is affected by the treatment assignment of those in their social network, is pervasive in real-world settings. However, it…
Staggered Rollout Designs Enable Causal Inference Under Interference Without Network Knowledge
Mayleen Cortez, Matthew Eichhorn, Christina Lee Yu
Randomized experiments are widely used to estimate causal effects across a variety of domains. However, classical causal inference approaches rely on critical independence assumpti…
Allocating with Priorities and Quotas: Algorithms, Complexity, and Dynamics
Siddhartha Banerjee, Matthew Eichhorn, David Kempe
In many applications such as rationing medical care and supplies, university admissions, and the assignment of public housing, the decision of who receives an allocation can be jus…