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 …

Propensity score matching and weighting are popular methods when estimating causal
effects in observational studies. Beyond the assumption of unconfoundedness, however …

The idea of covariate balance is at the core of causal inference. Inverse propensity weights
play a central role because they are the unique set of weights that balance the covariate …

We identify situations in which conditioning on text can address confounding in
observational studies. We argue that a matching approach is particularly well‐suited to this …

Covariate balancing propensity score by tailored loss functions Page 1 The Annals of Statistics
2019, Vol. 47, No. 2, 965–993 https://doi.org/10.1214/18-AOS1698 © Institute of Mathematical …

Collecting and analyzing observational data are essential to learning and implementing
lessons in earthquake engineering. Historically, the methods that have been used to …

Covariate balance is often advocated for objective causal inference since it mimics
randomization in observational data. Unlike methods that balance specific moments of …

Difference-in-differences designs are a powerful tool for causal inference in observational
settings where typical selection-on-observables assumptions fail to hold. When a pre …

We introduce trajectory balancing, a general reweighting approach to causal inference with
time-series cross-sectional (TSCS) data. We focus on settings in which one or more units is …

We study the problem of observational causal inference with continuous treatment. We focus
on the challenge of estimating the causal response curve for infrequently-observed …