As opaque predictive models increasingly impact many areas of modern life,
interest in quantifying the importance of a given input variable for making a
specific prediction has grown. Recently, there has been a proliferation of
model-agnostic methods to measure variable importance (VI) that analyze the
difference in predictive power between a full model trained on all variables
and a reduced model that excludes the variable(s) of interest. A bottleneck
common to these methods is the estimation of the reduced model for each
variable (or subset of variables), which is an expensive process that often
does not come with theoretical guarantees. In this work, we propose a fast and
flexible method for approximating the reduced model with important inferential
guarantees. We replace the need for fully retraining a wide neural network by a
linearization initialized at the full model parameters. By adding a ridge-like
penalty to make the problem convex, we prove that when the ridge penalty
parameter is sufficiently large, our method estimates the variable importance
measure with an error rate of $O(\frac{1}{\sqrt{n}})$ where $n$ is the number
of training samples. We also show that our estimator is asymptotically normal,
enabling us to provide confidence bounds for the VI estimates. We demonstrate
through simulations that our method is fast and accurate under several
data-generating regimes, and we demonstrate its real-world applicability on a
seasonal climate forecasting example.

本研究提出了一种快速且灵活的方法，用于近似估算模型中某一输入变量对于做出特定预测的重要性，并证明了该方法的并发性。通过模拟和气候预测实例，证明了该方法的准确性和实用性。

大型神经网络中变量重要性的懒估计

Lazy Estimation of Variable Importance for Large Neural Networks

There has been a growing interest in model-agnostic methods that can make
deep learning models more transparent and explainable to a user. Some
researchers recently argued that for a machine to achieve a certain degree of
human-level explainability, this machine needs to provide human causally
understandable explanations, also known as causability. A specific class of
algorithms that have the potential to provide causability are counterfactuals.
This paper presents an in-depth systematic review of the diverse existing body
of literature on counterfactuals and causability for explainable artificial
intelligence. We performed an LDA topic modelling analysis under a PRISMA
framework to find the most relevant literature articles. This analysis resulted
in a novel taxonomy that considers the grounding theories of the surveyed
algorithms, together with their underlying properties and applications in
real-world data. This research suggests that current model-agnostic
counterfactual algorithms for explainable AI are not grounded on a causal
theoretical formalism and, consequently, cannot promote causability to a human
decision-maker. Our findings suggest that the explanations derived from major
algorithms in the literature provide spurious correlations rather than
cause/effects relationships, leading to sub-optimal, erroneous or even biased
explanations. This paper also advances the literature with new directions and
challenges on promoting causability in model-agnostic approaches for
explainable artificial intelligence.

本研究对可解释人工智能中反事实和因果关系的分类算法进行了系统考察，发现当前的模型无法促进因果性，提出了新的方向和挑战。

可解释人工智能中的反事实和因果：理论、算法和应用

Counterfactuals and Causability in Explainable Artificial Intelligence:  Theory, Algorithms, and Applications

In recent years, a large amount of model-agnostic methods to improve the
transparency, trustability and interpretability of machine learning models have
been developed. We introduce local feature importance as a local version of a
recent model-agnostic global feature importance method. Based on local feature
importance, we propose two visual tools: partial importance (PI) and individual
conditional importance (ICI) plots which visualize how changes in a feature
affect the model performance on average, as well as for individual
observations. Our proposed methods are related to partial dependence (PD) and
individual conditional expectation (ICE) plots, but visualize the expected
(conditional) feature importance instead of the expected (conditional)
prediction. Furthermore, we show that averaging ICI curves across observations
yields a PI curve, and integrating the PI curve with respect to the
distribution of the considered feature results in the global feature
importance. Another contribution of our paper is the Shapley feature
importance, which fairly distributes the overall performance of a model among
the features according to the marginal contributions and which can be used to
compare the feature importance across different models.

本文介绍了一种基于局部特征重要性的机器学习可解释性方法，提出了两个可视化工具：partial importance (PI) 和 individual conditional importance (ICI) plots，并引入了 Shapley feature importance，可以公平地分配模型性能，比较不同模型上的特征重要性。