Valid estimates for the number of SARS-CoV-2 infections is imperative for assessing the impact of the COVID-19 pandemic within specific populations. Here, we discuss ongoing efforts aimed at understanding the state of the pandemic in two different...
The goal of a well-controlled study is to remove unwanted variation when estimating the causal effect of the intervention of interest. Experiments conducted in the basic sciences frequently achieve this goal using experimental controls, such as "negative'...
The bulk of causal inference studies rules out the presence of interference between units. However, in many real-world scenarios units are interconnected by social, physical or virtual ties and the effect of a treatment can spill from one unit to other...
Conjoint analysis is a popular experimental design used to measure multidimensional preferences. Researchers examine how varying a factor of interest, while controlling for other relevant factors, impacts decision-making. Currently, there exist two...
In a pilot program during the 2016-17 admissions cycle, the University of California, Berkeley invited many applicants for freshman admission to submit letters of recommendation. We use this pilot as the basis for an observational study of the impact of...
A causal inference revolution has been under way in political methodology for the better part of the last decade. Time series analysts have not been major contributors to this revolution because the tools that have been developed thus far do not fit our...
Many networks in political and social research are naturally bipartite — with two distinct types of actors (nodes), and edges connecting exclusively across the actor types. An example of such networks is the one that results from cosponsorship decisions...
In the presence of heterogeneous treatment effects, instrumental variable (IV) estimation point identifies the local average treatment effect, an average treatment effect (ATE) for compliers. This paper provides a set of identification results that...
In this talk I describe new, highly efficient estimators of optimal joint testing and treatment regimes under the no direct effect assumption that a given laboratory, diagnostic, or screening test has no effect on a patient's clinical outcomes, except...
Marginal structural models are a popular tool for investigating the effects of time-varying treatments, but they require the assumption that there are no unobserved confounders between the treatment and outcome. With observational data, this assumption...
