With the spread and rapid advancement of black box machine learning models,
the field of interpretable machine learning (IML) or explainable artificial
intelligence (XAI) has become increasingly important over the last decade. This
is particularly relevant for survival analysis, where the adoption of IML
techniques promotes transparency, accountability and fairness in sensitive
areas, such as clinical decision making processes, the development of targeted
therapies, interventions or in other medical or healthcare related contexts.
More specifically, explainability can uncover a survival model's potential
biases and limitations and provide more mathematically sound ways to understand
how and which features are influential for prediction or constitute risk
factors. However, the lack of readily available IML methods may have deterred
medical practitioners and policy makers in public health from leveraging the
full potential of machine learning for predicting time-to-event data. We
present a comprehensive review of the limited existing amount of work on IML
methods for survival analysis within the context of the general IML taxonomy.
In addition, we formally detail how commonly used IML methods, such as such as
individual conditional expectation (ICE), partial dependence plots (PDP),
accumulated local effects (ALE), different feature importance measures or
Friedman's H-interaction statistics can be adapted to survival outcomes. An
application of several IML methods to real data on data on under-5 year
mortality of Ghanaian children from the Demographic and Health Surveys (DHS)
Program serves as a tutorial or guide for researchers, on how to utilize the
techniques in practice to facilitate understanding of model decisions or
predictions.

通过对现有的解释性机器学习方法的综述，我们详细介绍了如何将常用的解释性机器学习方法应用于生存分析，以实现对模型决策或预测的理解。并且通过对加纳儿童 5 岁以下死亡率的实际数据的应用，展示了如何在实践中使用这些技术来促进对模型决策或预测的理解。

可解释的机器学习用于生存分析

Interpretable Machine Learning for Survival Analysis

We study the problem of inferring heterogeneous treatment effects from
time-to-event data. While both the related problems of (i) estimating treatment
effects for binary or continuous outcomes and (ii) predicting survival outcomes
have been well studied in the recent machine learning literature, their
combination -- albeit of high practical relevance -- has received considerably
less attention. With the ultimate goal of reliably estimating the effects of
treatments on instantaneous risk and survival probabilities, we focus on the
problem of learning (discrete-time) treatment-specific conditional hazard
functions. We find that unique challenges arise in this context due to a
variety of covariate shift issues that go beyond a mere combination of
well-studied confounding and censoring biases. We theoretically analyse their
effects by adapting recent generalization bounds from domain adaptation and
treatment effect estimation to our setting and discuss implications for model
design. We use the resulting insights to propose a novel deep learning method
for treatment-specific hazard estimation based on balancing representations. We
investigate performance across a range of experimental settings and empirically
confirm that our method outperforms baselines by addressing covariate shifts
from various sources.

本文研究了从时事件数据中推断异质性处理效应的问题，主要关注学习（离散时间）处理特定条件风险函数，并提出了一种基于平衡表示的新型深度学习方法来解决来自各种来源的协变量偏移问题。

从时间事件数据中学习异质性治疗效应的 SurvITE 模型

SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event  Data

This article introduces the pammtools package, which facilitates data
transformation, estimation and interpretation of Piece-wise exponential
Additive Mixed Models. A special focus is on time-varying effects and
cumulative effects of time-dependent covariates, where multiple past
observations of a covariate can cumulatively affect the hazard, possibly
weighted by a non-linear function. The package provides functions for
convenient simulation and visualization of such effects as well as a robust and
versatile function to transform time-to-event data from standard formats to a
format suitable for their estimation. The models can be represented as
Generalized Additive Mixed Models and estimated using the R package mgcv. Many
examples on real and simulated data as well as the respective R code are
provided throughout the article.

该论文介绍了 pammtools 软件包，它可以方便地进行数据转换、估计和解释 Piece-wise exponential Additive Mixed Models，重点关注时间变化效应和时间依赖协变量的累积效应，并提供了 Simulation 和可视化函数来模拟和可视化此类效应以及适合于估计的标准格式到格式的时间事件数据转换的 Robust and versatile 函数。该模型可以表示为广义加性混合模型，并使用 R 包 mgcv 估计，全文提供了许多实际和模拟数据的示例以及相应的 R 代码。