This article reviews recent advances in causal inference relevant to sociology. We focus on
a selective subset of contributions aligning with four broad topics: causal effect identification …

Much research effort is invested in attempting to determine causal influences on disease
onset and progression to inform prevention and treatment efforts. However, this is often …

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 …

When randomized experiments are infeasible, quasi-experimental designs can be exploited
to evaluate causal treatment effects. The strongest quasi-experimental designs for causal …

Directed acyclic graphs (DAGs), a prominent tool for expressing assumptions in
epidemiologic research, are most useful when the hypothetical data generating structure is …

Graphical models are useful tools in causal inference, and causal directed acyclic graphs
(DAGs) are used extensively to determine the variables for which it is sufficient to control for …

Participants in epidemiologic and genetic studies are rarely true random samples of the
populations they are intended to represent, and both known and unknown factors can …

For misguided reasons, social scientists have long been reluctant to use gain scores for
estimating causal effects. This article develops graphical models and graph-based …

Replication has long been a cornerstone for establishing trustworthy scientific results, but
there remains considerable disagreement about what constitutes a replication, how results …

Interrupted time series designs are a valuable quasi-experimental approach for evaluating
public health interventions. Interrupted time series extends a single group pre-post …