Unmeasured confounding poses a significant challenge in identifying and estimating causal effects across various research domains. Existing methods to address confounding often rely on either parametric models or auxiliary variables, which strongly rest on domain knowledge and could be fairly restrictive in practice. In this paper, we propose a novel strategy for identifying causal effects  in the presence of confounding under an additive structural equation with light-tailed confounding. This strategy uncovers the causal effect by exploring the relationship between the exposure and outcome at the extreme, which can bypass the need for parametric assumptions and auxiliary variables. The resulting identification is versatile, accommodating a multi-dimensional exposure, and applicable in scenarios involving unmeasured confounders, selection bias, or measurement errors. Building on this identification approach, we develop an Extreme-based Causal Effect Learning (EXCEL) method and further establish its consistency and non-asymptotic error bound. The asymptotic normality of the proposed estimator is established under the linear model. The EXCEL method is applied to causal inference problems with invalid instruments to construct a valid confidence set for the causal effect. Simulations and a real data analysis are used to illustrate the  potential application of our method in  causal inference.