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research

My dissertation is on learning to design, deliver, and diffuse interventions. I’m affiliated with the Initiative on the Digital Economy, Social Analytics Lab, Behavioral Research Lab at MIT, Harvard Data Science Initiative and the American Statistical Association.

working papers:

First Law of Motion:
Influencer Video Advertising on TikTok
Jeremy Yang^*, Juanjuan Zhang, Yuhan Zhang

[paper] [slides] [talk] [tweet] [media]

Major Revision at Marketing Science

- JMS Annual Conference Best Paper Award (2021/11)
- MSI Alden G. Clayton Doctoral Dissertation Proposal Award (2021/5)
- MIT Sloan Doctoral Research Forum Thesis Prize (2021/4)

Abstract: This paper develops an algorithm to predict the effect of influencer video advertising on product sales. We propose the concept of motion-score, or m-score, a summary statistic that captures the extent to which a product is advertised in the most engaging parts of a video. We locate pixel-level product placement with an object detection algorithm and estimate pixel-level engagement as a saliency map by fine-tuning a deep 3D convolutional neural network on video-level engagement data. M-score is then defined as pixel-level engagement-weighted advertising intensity of a video. We construct and evaluate the algorithm with around 40,000 influencer video ads on TikTok, the largest short video platform of the world. We leverage variation in video posting time to identify the causal effect of video ads on product sales. Videos of higher m-score indeed lift more sales. This effect is sizable, robust, and more pronounced among impulsive, hedonic, or inexpensive products. We trace the mechanism to influencers’ incentives to promote themselves rather than the product. We discuss how various stakeholders in entertainment commerce can use m-score in a scalable way to optimize content, align incentives, and improve efficiency.

seminar and conference talks:
- Core Data Science, Meta (2021/12)
- Bauer College of Business, University of Houston (2021/11)
- Marketing Science, TikTok (2021/7)
- Theory + Practice in Marketing, Wharton (2021/6)
- Marketing Science Conference, University of Rochester (2021/6)
- IDE Annual Conference, MIT (2021/5)
- Artificial Intelligence in Management Conference, USC (2021/5)
- Sloan Doctoral Research Forum, MIT (2021/4)
- Paris Conference on Digital Economics Workshop (2021/4)
- Management Workshop, Peking University (2021/3)
- Rotterdam School of Management, Erasmus University (2021/3)
- Columbia Business School, Columbia University (2021/2)
- ICCIT & School of Management, University of Toronto (2021/2)
- Darden School of Business, University of Virginia (2021/2)
- School of Business, University of Washington (2021/1)
- Cheung Kong Graduate School of Business (2021/1)
- Harvard Business School, Harvard University (2021/1)
- HEC Paris & Hi! Paris Center on AI and Data Analytics (2021/1)
- Krannert School of Management, Purdue University (2021/1)
- College of Business, City University of Hong Kong (2021/1)
- Big Data in Mobile Analytics, Temple University (2020/12)
- Conference on AI/ML, NYU, CMU & Temple University (2020/12)
- HKU Business School, University of Hong Kong (2020/11)
- Conference on Digital Experimentation, MIT (2020/11)
- Marketing Seminar, MIT (2020/11)
- HBS Digital Doctoral Workshop, Harvard University (2020/11)

Targeting for Long-Term Outcomes
Jeremy Yang, Dean Eckles, Paramveer Dhillon, Sinan Aral

[paper] [slides] [talk] [tweet] [media]

Minor Revision at Management Science

- American Statistical Association Dissertation Award (2021/1)
- INFORMS Annual Meeting Best Paper Award (2020/11)
- Accepted for Presentation at the NeurIPS Conference (2019/12)

Abstract: Decision-makers often want to target interventions (e.g., marketing campaigns) so as to maximize an outcome that is observed only in the long-term. This typically requires delaying decisions until the outcome is observed or relying on simple short-term proxies for the long-term outcome. Here we build on the statistical surrogacy and off-policy learning literature to impute the missing long-term outcomes and then approximate the optimal targeting policy on the imputed outcomes via a doubly-robust approach. We apply our approach in large-scale proactive churn management experiments at The Boston Globe by targeting optimal discounts to its digital subscribers to maximize their long-term revenue. We first show that conditions for validity of average treatment effect estimation with imputed outcomes are also sufficient for valid policy evaluation and optimization; furthermore, these conditions can be somewhat relaxed for policy optimization. We then validate this approach empirically by comparing it with a policy learned on the ground truth long-term outcomes and show that they are statistically indistinguishable. Our approach also outperforms a policy learned on short-term proxies for the long-term outcome. In a second field experiment, we implement the optimal targeting policy with additional randomized exploration, which allows us to update the optimal policy for each new cohort of customers to account for potential non-stationarity. Over three years, our approach had a net-positive revenue impact in the range of $4-5 million compared to The Boston Globe’s current policies.

seminar and conference talks:
- Core Data Science, Meta (2021/12)
- Causal Inference Symposium, RAND Corporation (2021/6)
- American Economic Association Annual Meeting (2021/1)
- School of Business, Hebrew University of Jerusalem (2020/12)
- Workshop on Information Systems and Economics (2020/12)
- Inference and Statistics Reading Group, Lyft (2020/12)
- INFORMS Annual Meeting (2020/11)
- Conference on Computational Social Science, MIT (2020/7)
- IDE Annual Conference, MIT (2020/5)
- Marketing Seminar, MIT (2020/5)
- NeurIPS CausalML Workshop (2019/12)
- HBS Digital Doctoral Workshop, Harvard University (2019/12)
- Conference on Digital Experimentation, MIT (2019/11)
- Advances on Field Experiments, University of Chicago (2019/7)

publications:

Interdependence and the Cost of Uncoordinated Responses
to COVID-19
David Holtz, Michael Zhao, Seth G. Benzell, Cathy Y. Cao, M. Amin Rahimiana, Jeremy Yang, Jennifer Allen, Avinash Collis, Alex Moehring, Tara Sowrirajan, Dipayan Ghosha, Yunhao Zhang, Paramveer S. Dhillon, Christos Nicolaides, Dean Eckles, Sinan Aral

Proceedings of the National Academy of Sciences (2020) [paper]

Abstract: Social distancing is the core policy response to COVID-19. But as federal, state and local governments begin opening businesses and relaxing shelter-in-place orders worldwide, we lack quantitative evidence on how policies in one region affect mobility and social distancing in other regions and the consequences of uncoordinated regional policies adopted in the presence of such spillovers. We therefore combined daily, county-level data on shelter-in-place and business closure policies with movement data from over 27 million mobile devices, social network connections among over 220 million of Facebook users, daily temperature and precipitation data from 62,000 weather stations and county-level census data on population demographics to estimate the geographic and social network spillovers created by regional policies across the United States. Our analysis showed the contact patterns of people in a given region are significantly influenced by the policies and behaviors of people in other, sometimes distant, regions. When just one third of a state’s social and geographic peer states adopt shelter in place policies, it creates a reduction in mobility equal to the state’s own policy decisions. These spillovers are mediated by peer travel and distancing behaviors in those states. A simple analytical model calibrated with our empirical estimates demonstrated that the “loss from anarchy” in uncoordinated state policies is increasing in the number of non-cooperating states and the size of social and geographic spillovers. These results suggest a substantial cost of uncoordinated government responses to COVID-19 when people, ideas, and media move across borders.

How Do Successful Scholars Get their Best Research Ideas?
An Exploration
Cathy Cao, Xinyu Cao, Matthew Cashman, Madhav Kumar, Artem Timoshenko, Jeremy Yang^*, Shuyi Yu, Jerry Zhang, Yuting Zhu, Birger Wernerfelt

Marketing Letters (2019) [paper]

Abstract: We interview 24 marketing professors to ask how they got the ideas for 64 of their papers. More than three quarters of the papers were inspired by holes in the literature, by a “stylized fact” that the current literature cannot explain, or by an interaction with a manager. The rest fall into several smaller categories that to a large extent can be seen as special cases of the three big ones. We describe how papers from each of the three big categories help move the literature forward. We also illustrate the range of situations contained in each category by way of several examples. Among the authors we interview, most do not use a single source. As these authors become more senior, managerial contacts play an increasing role, while the balance between literature and stylized facts appears to be unchanged.

^*Authors are listed in an alphabetic order

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