Alexander Peysakhovich

Learning causal effects from many randomized experiments using regularized instrumental variables

Scientific and business practices are increasingly resulting in large collections of randomized experiments. Analyzed together multiple experiments can tell us things that individual experiments cannot. We study how to learn causal relationships between variables from the kinds of collections faced by modern data scientists: the number of experiments is large, many experiments have very small effects, and the analyst lacks metadata (e.g., descriptions of the interventions). We use experimental groups as instrumental variables (IV) and show that a standard method (two-stage least squares) is biased even when the number of experiments is infinite. We show how a sparsity-inducing l0 regularization can (in a reversal of the standard bias--variance tradeoff) reduce bias (and thus error) of interventional predictions. We are interested in estimating causal effects, rather than just predicting outcomes, so we also propose a modified cross-validation procedure (IVCV) to feasibly select the regularization parameter. We show, using a trick from Monte Carlo sampling, that IVCV can be done using summary statistics instead of raw data. This makes our full procedure simple to use in many real-world applications.

Learning context-dependent preferences from raw data

A large body of existing work in social science as well as computer science attempts to infer preferences of individuals from the actions they take. This includes research areas such as industrial organization [4], marketing [1], political science [12], analysis of auctions [3], recommender systems [8], search engine ranking [9], and many others. The workhorse model used either implicitly or explicitly in these disparate literatures is the rational choice model.