Analog-mixed-signal (AMS) circuits are highly non-linear and operate on continuous real-world signals, making them far more difficult to model with data-driven AI than digital blocks. To close the gap between structured design data (device dimensions, bias voltages, etc.) and real-world performance, we propose a causal-inference framework that first discovers a directed-acyclic graph (DAG) from SPICE simulation data and then quantifies parameter impact through Average Treatment Effect (ATE) estimation. The approach yields human-interpretable rankings of design knobs and explicit 'what-if' predictions, enabling designers to understand trade-offs in sizing and topology. We evaluate the pipeline on three operational-amplifier families (OTA, telescopic, and folded-cascode) implemented in TSMC 65nm and benchmark it against a baseline neural-network (NN) regressor. Across all circuits the causal model reproduces simulation-based ATEs with an average absolute error of less than 25%, whereas the neural network deviates by more than 80% and frequently predicts the wrong sign. These results demonstrate that causal AI provides both higher accuracy and explainability, paving the way for more efficient, trustworthy AMS design automation.

翻译：模拟混合信号（AMS）电路具有高度非线性特性，且处理连续的真实世界信号，这使得基于数据驱动的人工智能对其建模的难度远高于数字模块。为弥合结构化设计数据（器件尺寸、偏置电压等）与实际性能之间的差距，本文提出一种因果推断框架：该框架首先从SPICE仿真数据中发现有向无环图（DAG），然后通过平均处理效应（ATE）估计量化参数影响。该方法可生成设计参数的可解释排序及明确的"what-if"预测，使设计者能够理解尺寸与拓扑结构中的权衡关系。我们在TSMC 65nm工艺实现的三种运算放大器族（OTA、套筒式共源共栅、折叠共源共栅）上评估该流水线，并以基线神经网络回归器作为基准。在所有电路上，因果模型复现基于仿真的ATE时，平均绝对误差低于25%，而神经网络的偏差超过80%且频繁预测错误符号。这些结果表明，因果AI在提供更高精度的同时兼具可解释性，为更高效、更可靠的AMS设计自动化铺平了道路。