Paccar Hall, Room 294
Seminar Speaker: Jinyang Zheng
Affiliation: University of Washington
Area: Information Systems
Name of Presentation: How Mega Is the Mega? Measuring the Spillover Effects of WeChat by Machine Learning and Econometrics
WeChat, an instant messaging app, is considered a mega app due to its dominance in terms of usage among Chinese smartphone users. Nevertheless, little is known about its externality in regard to the broader app market. Our work estimates the spillover effects of WeChat on the other top-50 most frequently used apps in China through data on users’ weekly app usage. Given the challenge of determining causal inference from observational data, we apply a graphical model and econometrics to estimate the spillover effects through two steps: (1) we determine the causal structure by estimating a partially ancestral diagram, using a Fast Causal Inference (FCI) algorithm; (2) given the causal structure, we find a valid adjustment set and estimate the causal effects by an econometric model with the adjustment set as controlling non-causal effects. Our findings show that the spillover effects of WeChat are limited; in fact, only two other apps, Tencent News and Taobao, receive positive spillover effects from WeChat. In addition, we show that, if researchers fail to account for the causal structure that we determined from the graphical model, it is easy to fall into the trap of confounding bias and selection bias when estimating causal effects.