We present a novel framework for estimation and inference for the broad class of universal approximators. Estimation is based on the decomposition of model predictions into Shapley values. Inference relies on analyzing the bias and variance properties of individual Shapley components. We show that Shapley value estimation is asymptotically unbiased, and we introduce Shapley regressions as a tool to uncover the true data generating process from noisy data alone. The well-known case of the linear regression is the special case in our framework if the model is linear in parameters. We present theoretical, numerical, and empirical results for the estimation of heterogeneous treatment effects as our guiding example.

翻译：我们提出了一种针对通用逼近器广泛类别的新型估计与推断框架。估计基于将模型预测分解为沙普利值。推断依赖于分析各沙普利分量的偏差与方差特性。我们证明沙普利值估计具有渐近无偏性，并引入沙普利回归作为仅从含噪数据中揭示真实数据生成过程的工具。若模型在参数上呈线性，则线性回归这一经典案例将成为我们框架中的特例。我们以异质处理效应的估计作为指导性示例，呈现了理论、数值与实证结果。