Knowledge of causal effects is of great importance to decision makers in a wide variety of
settings. In many cases, however, these causal effects are not known to the decision makers …

We survey the recent, fast-growing literature on peer effects in networks. An important
recurring theme is that the causal identification of peer effects depends on the structure of …

The use of statistical significance and p-values has become a matter of substantial
controversy in various fields using statistical methods. This has gone as far as some journals …

This paper provides an assessment of the early contributions of machine learning to
economics, as well as predictions about its future contributions. It begins by briefly …

In this chapter, we present econometric and statistical methods for analyzing randomized
experiments. For basic experiments, we stress randomization-based inference as opposed …

Social distancing is the core policy response to coronavirus disease 2019 (COVID-19). But,
as federal, state and local governments begin opening businesses and relaxing shelter-in …

In this essay I discuss potential outcome and graphical approaches to causality, and their
relevance for empirical work in economics. I review some of the work on directed acyclic …

To what extent are democratic elections vulnerable to social media manipulation? The
fractured state of research and evidence on this most important question facing democracy …

We develop new tools for estimating the causal effects of treatments or instruments that
combine multiple sources of variation according to a known formula. Examples include …

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