We propose a method for causal effect estimation in settings with hidden confounders, via spectral representations derived from proxy variables. By formulating the problem in a reproducing kernel Hilbert space and leveraging operator-theoretic tools, we derive tractable estimators that recover causal effects without requiring direct access to the confounder. Our approach generalizes instrumental variable and proxy-based methods, and we demonstrate both theoretical guarantees and strong empirical performance across synthetic and real-world benchmarks.