The continuity assumption is a critical concept in causal inference, especially within regression discontinuity designs. It posits that the potential outcomes of a treated unit and a control unit would be the same in the absence of treatment, as long as they are near the cutoff point. This assumption ensures that any observed changes in outcomes can be attributed to the treatment rather than other confounding factors, making it essential for establishing causality.
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