Weekly highlights of recent papers and empirical findings from economics, health, policy, and the social sciences that apply causal methods and report meaningful results.
This issue highlights five papers at the intersection of machine learning and causal inference, focusing on heterogeneous treatment effects, policy learning, and debiased estimation. These papers represent the current frontier of causal machine learning.
This issue highlights four recent empirical papers in education and development economics that use rigorous causal identification strategies to study long-run human capital outcomes.
This issue summarises four important recent studies that use instrumental variables and natural experiments to address high-stakes empirical questions in labour economics, trade, and health policy.
This issue highlights four important studies using regression discontinuity designs across health, education, and criminal justice policy. Each illustrates a different facet of RD methodology while delivering substantive empirical findings.
Shift-share IV, the China syndrome, valid t-ratios, and macro identification—a roundup of the most important recent IV results.
Four landmark RD papers: CCT (2014), Dell (2010), Angrist–Lavy (1999), and Londono-Velez et al. (2020)—what they found and why they matter.