Causal inference is a critical research topic across many domains, such as statistics,
computer science, education, public policy, and economics, for decades. Nowadays …

Confounding can cause substantial bias in nonexperimental studies that aim to estimate
causal effects. Propensity score methods allow researchers to reduce bias from measured …

The propensity score plays a central role in a variety of causal inference settings. In
particular, matching and weighting methods based on the estimated propensity score have …

Continuous treatments propensity scoring remains understudied as the majority of methods
are focused on the binary treatment setting. Current propensity score methods for …

For many machine learning algorithms, two main assumptions are required to guarantee
performance. One is that the test data are drawn from the same distribution as the training …

We use plausibly exogenous variation in the availability of the Russian analog television
signal in Ukraine to study how a media source with a conspicuous political agenda impacts …

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 …

The relationship between students' subject-specific academic self-concept and their
academic achievement is one of the most widely researched topics in educational …

Recent developments in the causal-inference literature have renewed psychologists' interest
in how to improve causal conclusions based on observational data. A lot of the recent writing …

Rising temperatures are expected to stall progress on food insecurity by reducing
agricultural yields in the coming decades. But hot periods may also increase food insecurity …