A0225
Title: Design-based inference under random potential outcomes via Riesz representation
Authors: Yukai Yang - Uppsala University (Sweden) [presenting]
Abstract: A design-based framework is introduced for causal inference that accommodates random potential outcomes, thereby extending the classical Neyman-Rubin model in which outcomes are treated as fixed. Each unit's potential outcome is modeled as a structural mapping $\tilde{y}_i(z, \omega)$, where $z$ denotes the treatment assignment and $\omega$ represents latent outcome-level randomness. Inspired by recent connections between design-based inference and the Riesz representation theorem, potential outcomes are embedded in a Hilbert space and define treatment effects as linear functionals, yielding estimators constructed via their Riesz representers. This approach preserves the core identification logic of randomized assignment while enabling valid inference under stochastic outcome variation. Large-sample properties are established under local dependence, and consistent variance estimators that remain valid under weaker structural assumptions are developed, including partially known dependence. A simulation study illustrates the robustness and finite-sample behavior of the estimators. Overall, the framework unifies design-based reasoning with stochastic outcome modeling, broadening the scope of causal inference in complex experimental settings.
