Marginal Effects for Non-Linear Prediction Functions
非线性预测函数的边际效应AI 解读 ↓
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非线性预测函数的边际效应
- 研究问题
- 对于非线性模型和黑箱预测函数，如何用可解释的特征变化描述预测效应，并避免单一平均效应掩盖局部非线性？
- 方法与思路
- 作者提出以前向差分定义的边际效应，将其推广到多变量变化，引入非线性度量，并划分特征空间计算条件平均效应。
- 材料陈述的贡献
- 摘要提出前向边际效应、非线性度量和子空间条件平均效应，使预测函数的特征解释能够表达联合变化与局部差异。
- 阅读关注点
- 应核对前向变化幅度、联合特征变化及子空间划分的选择规则，以及非线性度量如何计算、解释和反映估计不确定性。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未提供评价数值或推断条件，预测函数的特征效应不能自动解释为因果效应。
原文摘要
Beta coefficients for linear regression models represent the ideal form of an interpretable feature effect. However, for non-linear models and especially generalized linear models, the estimated coefficients cannot be interpreted as a direct feature effect on the predicted outcome. Hence, marginal effects are typically used as approximations for feature effects, either in the shape of derivatives of the prediction function or forward differences in prediction due to a change in a feature value. While marginal effects are commonly used in many scientific fields, they have not yet been adopted as a model-agnostic interpretation method for machine learning models. This may stem from their inflexibility as a univariate feature effect and their inability to deal with the non-linearities found in black box models. We introduce a new class of marginal effects termed forward marginal effects. We argue to abandon derivatives in favor of better-interpretable forward differences. Furthermore, we generalize marginal effects based on forward differences to multivariate changes in feature values. To account for the non-linearity of prediction functions, we introduce a non-linearity measure for marginal effects. We argue against summarizing feature effects of a non-linear prediction function in a single metric such as the average marginal effect. Instead, we propose to partition the feature space to compute conditional average marginal effects on feature subspaces, which serve as conditional feature effect estimates.