Causal discovery procedures aim to deduce causal relationships among variables in a multivariate dataset. While various methods have been proposed for estimating a single causal model or a single equivalence class of models, less attention has been given to quantifying uncertainty in causal discovery in terms of confidence statements. The primary challenge in causal discovery […]

Join the Salem Center and Panos Toulis (Chicago Booth). Standard statistical inference in regression models, including bootstrap-based procedures, relies on assumptions on the asymptotics of the covariate/error distribution. These assumptions are generally strong—for example, they are typically violated by simple heavy-tailed distributions. In this talk, we propose a new paradigm of inference using randomization theory. Our main […]

A Causal Inference Seminar with Jared Murray (UT Austin). Bayesian models are a popular and effective tool for inferring the (possibly heterogeneous) effects of interventions. I will discuss how to carefully specify models and prior distributions to apply judicious regularization of heterogeneous effects. I will also discuss how to extract answers to scientific and policy […]

A Causal Inference Seminar with Magdalena Bennett. High-stake testing plays a crucial role in many educational systems, guiding policies of accountability, resource allocation, and even school choice. However, non-representative patterns of attendance can skew how useful these measures are for accomplishing their main objective. Are we really measuring the quality or performance of a school […]

A Causal Inference Seminar with Alex Volfovsky (Duke). Join us in person at RRH 4.408 or via Zoom here. Social media sites are often blamed for exacerbating political polarization by creating “echo chambers” that prevent people from being exposed to information that contradicts their preexisting beliefs. In our first field experiment Democrats and Republicans followed […]