A century ago, Neyman showed how to evaluate the efficacy of treatment using
a randomized experiment under a minimal set of assumptions. This classical
repeated sampling framework serves as a basis of routine experimental analyses
conducted by today's scientists across disciplines. In this paper, we
demonstrate that Neyman's methodology can also be used to experimentally
evaluate the efficacy of individualized treatment rules (ITRs), which are
derived by modern causal machine learning algorithms. In particular, we show
how to account for additional uncertainty resulting from a training process
based on cross-fitting. The primary advantage of Neyman's approach is that it
can be applied to any ITR regardless of the properties of machine learning
algorithms that are used to derive the ITR. We also show, somewhat
surprisingly, that for certain metrics, it is more efficient to conduct this
ex-post experimental evaluation of an ITR than to conduct an ex-ante
experimental evaluation that randomly assigns some units to the ITR. Our
analysis demonstrates that Neyman's repeated sampling framework is as relevant
for causal inference today as it has been since its inception.

本文展示了如何利用 Neyman 的方法通过实验证明个体化治疗规则 (ITRs) 的有效性，并解释了如何考虑交叉适配所引起的额外不确定性。该方法的主要优势在于，它适用于任何 ITR，不受用于推导 ITR 的机器学习算法特性的限制，并表明对于某些度量标准，进行 ITR 的后期实验证明比进行随机分配一些单位至 ITR 的前期实验证明更为高效。通过分析，证明了 Neyman 的重复抽样框架在因果推断中是与其创建以来一样相关的。

尼曼遇见因果机器学习：个体化治疗规则的实验评估

Neyman Meets Causal Machine Learning: Experimental Evaluation of  Individualized Treatment Rules

The effects of treatments are often heterogeneous, depending on the
observable characteristics, and it is necessary to exploit such heterogeneity
to devise individualized treatment rules (ITRs). Existing estimation methods of
such ITRs assume that the available experimental or observational data are
derived from the target population in which the estimated policy is
implemented. However, this assumption often fails in practice because of
limited useful data. In this case, policymakers must rely on the data generated
in the source population, which differs from the target population.
Unfortunately, existing estimation methods do not necessarily work as expected
in the new setting, and strategies that can achieve a reasonable goal in such a
situation are required. This study examines the application of distributionally
robust optimization (DRO), which formalizes an ambiguity about the target
population and adapts to the worst-case scenario in the set. It is shown that
DRO with Wasserstein distance-based characterization of ambiguity provides
simple intuitions and a simple estimation method. I then develop an estimator
for the distributionally robust ITR and evaluate its theoretical performance.
An empirical application shows that the proposed approach outperforms the naive
approach in the target population.

研究了采用分布鲁棒优化方法（distributionally robust optimization，DRO）来推断个性化治疗规则（individualized treatment rules，ITRs）的估计器在新设置中的应用，包括 Wasserstein distance-based ambiguity characterizations 和其在目标人群中的理论表现的评估。这个方法在目标人群中优于传统的策略。

基于 Wasserstein 距离的分布鲁棒策略学习

Distributionally Robust Policy Learning with Wasserstein Distance

Individualized treatment rules aim to identify if, when, which, and to whom
treatment should be applied. A globally aging population, rising healthcare
costs, and increased access to patient-level data have created an urgent need
for high-quality estimators of individualized treatment rules that can be
applied to observational data. A recent and promising line of research for
estimating individualized treatment rules recasts the problem of estimating an
optimal treatment rule as a weighted classification problem. We consider a
class of estimators for optimal treatment rules that are analogous to convex
large-margin classifiers. The proposed class applies to observational data and
is doubly-robust in the sense that correct specification of either a propensity
or outcome model leads to consistent estimation of the optimal individualized
treatment rule. Using techniques from semiparametric efficiency theory, we
derive rates of convergence for the proposed estimators and use these rates to
characterize the bias-variance trade-off for estimating individualized
treatment rules with classification-based methods. Simulation experiments
informed by these results demonstrate that it is possible to construct new
estimators within the proposed framework that significantly outperform existing
ones. We illustrate the proposed methods using data from a labor training
program and a study of inflammatory bowel syndrome.

本文介绍了通过重新定义分类问题，将最优治疗规则的估计转化为分类问题，提出了一类新的基于凸大间隔分类器的最优治疗规则估计方法，这类方法具有双重稳健性，能够在使用属性模型或结果模型进行正确说明时一致地估计最优个体化治疗规则，并使用半参数效率理论技术推导了提议的估计器的一致收敛率，并使用这些结果表征了基于分类的方法估计最优治疗规则时的偏差 - 方差折衷，并通过仿真实验证明了在提议的框架内构造的新估计器可以显著优于现有估计器，数据来自劳动培训计划和炎症性肠病研究。

利用观察数据进行个体化治疗规则的高效增强和松弛学习

Efficient augmentation and relaxation learning for individualized  treatment rules using observational data

Personalized medicine has received increasing attention among statisticians,
computer scientists, and clinical practitioners. A major component of
personalized medicine is the estimation of individualized treatment rules
(ITRs). Recently, Zhao et al. (2012) proposed outcome weighted learning (OWL)
to construct ITRs that directly optimize the clinical outcome. Although OWL
opens the door to introducing machine learning techniques to optimal treatment
regimes, it still has some problems in performance. In this article, we propose
a general framework, called Residual Weighted Learning (RWL), to improve finite
sample performance. Unlike OWL which weights misclassification errors by
clinical outcomes, RWL weights these errors by residuals of the outcome from a
regression fit on clinical covariates excluding treatment assignment. We
utilize the smoothed ramp loss function in RWL, and provide a difference of
convex (d.c.) algorithm to solve the corresponding non-convex optimization
problem. By estimating residuals with linear models or generalized linear
models, RWL can effectively deal with different types of outcomes, such as
continuous, binary and count outcomes. We also propose variable selection
methods for linear and nonlinear rules, respectively, to further improve the
performance. We show that the resulting estimator of the treatment rule is
consistent. We further obtain a rate of convergence for the difference between
the expected outcome using the estimated ITR and that of the optimal treatment
rule. The performance of the proposed RWL methods is illustrated in simulation
studies and in an analysis of cystic fibrosis clinical trial data.

本文提出了一种改进的人工智能医疗方法，叫残差加权学习，能够更好地优化治疗结果，提高疗效。