Detecting and quantifying confounding effects from data is a key challenge in causal inference. Existing methods frequently assume causal sufficiency, disregarding the presence of unobserved confounding variables. Causal sufficiency is both unrealistic and empirically untestable. Additionally, existing methods make strong parametric assumptions about the underlying causal generative process to guarantee the identifiability of confounding variables. Relaxing the above assumptions and leveraging recent advancements in causal discovery and confounding analysis with non-i.i.d. data, we propose a comprehensive approach for detecting and measuring confounding effects. We consider various definitions of confounding and introduce tailored methodologies to achieve three objectives: (i) detecting and quantifying confounding between pairs and multiple variables, (ii) separating unobserved confounding effects from total confounding effects, and (iii) understanding the relative strengths of confounding between different sets of variables. We examine some useful properties of our measures and present algorithms for measuring confounding. Our study provides a unified approach to measuring confounding, and our empirical results support the theoretical analysis.