讲座预告 | How Mega is the Mega? Measuring the Spillover Effects of WeChat by Machine Learning and Econometrics

讲座地点：25楼C区412

主讲人简介：窦一凡，复旦大学管理学院信息管理与信息系统系副教授。2007年毕业于清华大学经管学院，获管理学学士学位；2012年毕业于清华大学经管学院管理科学工程专业，获管理学博士学位。窦一凡的主要研究兴趣是与信息产品相关的经济学问题，如信息产品的定价策略、平台模式的定价和协调问题等。窦一凡的研究工作先后发表Information System Research、European Journal of Operational Research、管理科学学报等国内外知名学术期刊上，并入选ESI高被引论文。窦一凡曾获得Management Science的优秀reviewer服务奖，并于2018年获得国家自然科学基金优秀青年基金项目资助。

讲座纲要:WeChat, an instant messaging app, is considered a mega app due to its dominance in terms of use among Chinese smartphone users. Little is known, however, about its externality in the broader app market. This work estimates the spillover effects of WeChat on the other Top-50 most frequently used apps in China, using users’ weekly app usage data. Given the challenge of determining causal inference from observational data, we apply a graphical model and an econometric method to estimate the spillover effects in two steps: (1) we determine the causal structure by estimating a partially ancestral diagram, using a Fast Causal Inference (FCI) algorithm; and (2) given the causal structure, we find a valid adjustment set and estimate the causal effects by an econometric model with the adjustment set for 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 is determined from the graphical model, it is easy to fall into the trap of confounding bias and selection bias when estimating causal effects. The findings generate managerial implications in terms of app usage patterns, strategic management of mega apps on an app platform, and app promotional strategies for app platform managers and app developers.