We investigate large-sample properties of treatment effect estimators under unknown
interference in randomized experiments. The inferential target is a generalization of the …

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 …

Randomized experiments are widely used to estimate the causal effects of a proposed
treatment in many areas of science, from medicine and healthcare to the physical and …

We describe semiparametric estimation and inference for causal effects using observational
data from a single social network. Our asymptotic results are the first to allow for …

Randomized experiments, or A/B tests, are the standard approach for evaluating the causal
effects of new product features, ie, treatments. The validity of these tests rests on the" stable …

The weaponization of digital communications and social media to conduct disinformation
campaigns at immense scale, speed, and reach presents new challenges to identify and …

Randomized experiments are widely used to estimate causal effects across many domains.
However, classical causal inference approaches rely on independence assumptions that …

Traditional causal inference techniques assume data are independent and identically
distributed (IID) and thus ignores interactions among units. However, a unit's treatment may …

The global average treatment effect (GATE) is a primary quantity of interest in the study of
causal inference under network interference. With a correctly specified exposure model of …

Probabilistic Foundations of Statistical Network Analysis presents a fresh and insightful
perspective on the fundamental tenets and major challenges of modern network analysis. Its …