The debate on covid-19 origins has been politically fraught. Yet setting aside conspiracy theories and the most implausible of the lab-leak hypotheses, there is significant disagreement among qualified experts. Some are adamant that the case should be...
Intuition refers to the ability to use nonconscious information for conscious decision making. The nonconscious element has predominantly been measured by its speed of operation and ease of application. Only a few scholarly attempts at behavioral...
In recent decades, U.S. income and wealth inequality grew, educational attainment rose, and occupational structures shifted. Because these dimensions of social class are intertwined---with higher education often generating higher income, wealth, and...
The effects of refugee migration permeates most aspects of a recipient society, not least native inclusionary attitudes and behaviors towards refugees. While recent research has emphasized measuring the extent to which direct exposure to refugees affects...
We develop a simple cross-sectional research design to identify causal effects that is robust to unobservable heterogeneity. When many observational units are dense in physical space, it may be sufficient to regress the “spatial first differences” (SFD)...
A fundamental principle in the design of observational studies is to approximate the randomized experiment that would have been conducted under controlled circumstances. Across the health and social sciences, statistical methods for covariate adjustment...
Switchback experiments, where a firm sequentially exposes an experimental unit to random treatments, are among the most prevalent designs used in the technology sector, with applications ranging from ride-hailing platforms to online marketplaces...
Machine learning algorithms are now used to automate routine tasks and to guide high-stakes decisions, but, if not carefully designed, they can exacerbate inequities. I’ll start by describing an evaluation of automated speech recognition (ASR) tools...
A main research goal in various studies is to use an observational data set and provide a new set of counterfactual guidelines that can yield causal improvements. Dynamic Treatment Regimes (DTRs) are widely studied to formalize this process and enable...
There has been a growing interest in the use of machine learning methods for causal inference, which often involves adjusting or reappropriating predictive models, with causality in mind. As an alternative, anomaly detection methods offer a unique lens...
