Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 250 - The Future of Designed Experiments in the Era of Big Data
Type: Invited
Date/Time: Tuesday, August 4, 2020 : 1:00 PM to 2:50 PM
Sponsor: Quality and Productivity Section
Abstract #309387
Title: The Role of Additivity in Causal Inference Under Interference
Author(s): Daniel L Sussman* and Kelly Kung
Companies: Boston University and Boston University
Keywords: interference; causal inference; network
Abstract:

Minimizing assumptions in causal inference procedures is crucial to ensure valid conclusions but in the presence of interference, such minimal assumptions can lead to very small effective sample sizes. Incorporating additivity assumptions can enable the use of far more units. In this talk, we propose a framework for additivity across multiple components of a unit's exposure to treatments and demonstrate the potential improvements when these assumptions are valid. We explore the bias-variance trade-off inherent in these assumptions and argue that additivity assumptions can lead to smoother estimates of effect sizes. We conclude by considering how these assumptions impact the designs of experiments under network and other forms of interference.


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

Back to the full JSM 2020 program