A fundamental goal of scientific research is to learn about causal relationships. However,
despite its critical role in the life and social sciences, causality has not had the same …

Prior research revealed a striking heterogeneity of how consumers view smart objects, from
seeing them as helpful partners to merely a useful tool. We draw on mind perception theory …

Modeling what makes a request persuasive-eliciting the desired response from a reader-is
critical to the study of propaganda, behavioral economics, and advertising. Yet current …

We consider the problem of using observational data to estimate the causal effects of
linguistic properties. For example, does writing a complaint politely lead to a faster response …

NLP algorithms are increasingly used in computational social science to take linguistic
observations and predict outcomes like human preferences or actions. Making these social …

We present a neural network for predicting purchasing intent in an Ecommerce setting. Our
main contribution is to address the significant investment in feature engineering that is …

Product descriptions play an important role in the e-commerce ecosystem, conveying to
buyers information about a merchandise they may purchase. Yet, on leading e-commerce …

Estimating the effect of intervention from observational data while accounting for
confounding variables is a key task in causal inference. Oftentimes, the confounders are …

Nowadays, online shoppers have paid more and more attention to detailed product
descriptions, since a well-written description is a huge factor in making online sales …

The task of obfuscating writing style using sequence models has previously been
investigated under the framework of obfuscation-by-transfer, where the input text is explicitly …