Unconfoundedness Assumption Trend 2026

In causal inference, the unconfoundedness assumption posits that, after controlling for observed covariates, treatment assignment is independent of potential outcomes. This means no hidden variables bias the estimated effect. Researchers rely on it for matching, weighting, and regression. Economists, epidemiologists, and data scientists benefit, as it enables credible impact evaluation from observational data, not just randomized trials.