Regression models estimate associations conditional on covariates, often as a stepping-stone to causal claims. In social science, the challenge is not just fitting the model but understanding assumptions about linearity, independence, selection, and missingness.

Panel data allow researchers to observe the same units across time, making it possible to study change, persistence, and within-unit dynamics. However, panel models also face attrition, time-varying confounding, and unmodeled dependence. The frontier concerns dynamic treatment effects, staggered adoption, and models robust to heterogeneous trajectories.

Current quantitative work increasingly uses causal machine learning, Bayesian hierarchical models, synthetic controls, and measurement models that integrate text, networks, and geospatial data. A major open problem is to combine predictive performance with interpretability and credible causal identification.