The unconfoundedness assumption posits that, given a set of observed covariates, potential outcomes are independent of the treatment assignment. This means that any differences in outcomes between treated and untreated individuals can be attributed to the treatment itself, rather than to other variables that might confound the results. This assumption is crucial for estimating both average treatment effects and conditional average treatment effects accurately.
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