This paper studies nonparametric estimation of treatment and spillover effects using observational data from a single large network. We consider a model in which interference decays with network distance, which allows for peer influence in both outcomes and selection into treatment. Under this model, the total network and covariates of all units constitute sources of confounding, in contrast to existing work that assumes confounding can be summarized by a known, low-dimensional function of these objects. We propose to use graph neural networks to estimate the high-dimensional nuisance functions of a doubly robust estimator. We establish a network analog of approximate sparsity to justify the use of shallow architectures.
Digital platforms have revolutionized the way illegal drug trafficking is taking place. Modern drug dealers use social network platforms, such as Instagram and TikTok, as direct-to-consumer marketing tools. But apart from the marketing side, drug dealers also use fintech payment apps to engage in financial transactions with their clients. In this work, we leverage a large dataset from Venmo to investigate the digital money trail of drug dealers and the social networks they create. Using text and social network analytics, we identify two types of illicit users: mixed-activity participants and heavy drug traffickers, and build a random forest classifier that accurately predicts both types of illicit nodes. We then investigate the social network structure of drug dealers on Venmo and find that heavy drug traffickers share similar network characteristics with previous literature findings on drug trafficking networks. However, mixed-activity participants exhibit different patterns of network structure characteristics, including a higher clustering coefficient, suggesting that they may be accessing multiple networks and bridging those networks through their illicit activities. Our findings highlight the importance of distinguishing between these two types of illicit users and provide law enforcement agencies with valuable insights that can aid in combating illegal drug transactions in digital payment apps.
We empirically investigate the harbinger of failure phenomenon in the motion picture industry by analyzing the pre-release reviews written on movies by film critics. We find that harbingers of failure do exist. Their positive pre-release movie reviews provide a strong predictive signal that the movie will turn out to be a flop. This signal persists even for the top critic category, which usually consists of professional reviewers, indicating that having expertise in a professional domain does not necessarily lead to correct predictions. Our findings challenge the current belief that positive reviews always help enhance box office revenue. Moreover, they shed new light on the influencer reviewer hypothesis, which asks whether critics are indeed influencing the popularity of a movie or if they are just able to predict its popularity. We observe that, at least in a pre-release setting, harbinger critics are not influencing the outcome but rather mispredicting it, since if the opposite was true harbingers' reviews could turn a flop movie into a success. We further analyze the writing style of harbingers and provide new insights into their personality traits and cognitive biases.
How can you know which of your newly acquired customers will turn out to be good customers for your company? The standard approach to making such predictions involves collecting data on a user's past behavior and building statistical models to extrapolate a user's actions into the future. However, this method fails in the case of newly acquired customers where you have little behavioral or transactional data. Drawing inferences about a new user's future behavior in the absence of any historical data is known as the "cold-start" problem. This work introduces a new approach to solving the cold-start problem in networked services. By incorporating social network information, we demonstrate the ability to get improved forecasts regarding future customer behavior immediately after acquiring new customers.
This work investigates the structure and evolution of Venmo, a peer-to-peer payment application. Venmo is a unique social network in the sense that the edges among nodes represent financial transactions among individuals who shared an offline social interaction. We had two important findings. First, the degree distributions do not follow a power-law distribution, confirming previous studies that real-world social networks are rarely scale-free. Second, we examine the "topological" version of the small-world hypothesis and find that Venmo users are separated by a mean of 5.9 steps and a median of 6 steps confirming Milgram's hypothesis.
This is the outcome of our group's participation in the 11th triennial choice symposium. Our group discussed the emerging topic of data ethics and the choices we have as individuals. Our paper explores how data policies drive technological, organizational, and economic decisions made by digital platforms and consumers, and highlights the need for education on digital and data interactions.