CISE Seminar: Yuan Yuan, Purdue University

Date: Sept 9, 2022
Time: 3:00PM-4:00PM

Yuan Yuan
Assistant Professor
Purdue University

Using Causal Network Motifs to Characterize Network Interference in Randomized Controlled Experiments

Randomized control trials, or “A/B tests”, have been crucial to understanding the impact of an intervention. Traditional causal inference methods rely on a critical assumption called the “stable unit treatment value assumption” (SUTVA), which assumes that a user’s treatment response only depends on only their treatment assignment. However, SUTVA is an unrealistic assumption in settings such as networks, where a unit’s response may be affected by other users. This violation of SUTVA is referred to as network interference. The current literature on network interference has two major limitations — failing to account for social theories of interference (e.g. structural diversity or social contagion) and relying on human experts to model interference patterns. To tackle these issues, we propose a two-part machine learning approach to automatically characterize network interference conditions based on both the local network structures and the treatment assignment among users in the network neighborhood. Specifically, we first construct network motifs with treatment assignment information, referred to as causal network motifs, to characterize the network interference conditions for each unit. We then develop machine learning methods based on decision trees and nearest neighbors to map these causal network motif representations to an “exposure condition” proposed by Aronow and Samii (2017). We demonstrate the validity of our approach on simulated A/B tests on real-world networks and a re-analysis from a prior experiment study, which show how causal network motifs are able to more accurately account for complex interference patterns and reduce bias for treatment effect estimation with the presence of interference.

Yuan Yuan is a Computational Social Scientist and an Assistant Professor at the Krannert School of Management (MIS area) at Purdue University. Yuan researches social interactions and social networks for social well-being. He is especially interested in how to utilize social preference and social contagion to promote positive social interactions, and how social networks have shaped human behavior and can be reshaped by digital technologies. His research also aims to advance the methodology in computational social science by drawing upon quantitative methods such as machine learning, causal inference, experimental design, and network science. Yuan received his PhD in Institute for Data, Systems, and Society (IDSS) at Massachusetts Institute of Technology and prior to MIT, received his Bachelor’s degree in Computer Science and Economics from Tsinghua University.

Faculty Host: Jinglong Zhao
Student Host: Erfan Aasi