Estimating how a treatment affects different individuals, known as
heterogeneous treatment effect estimation, is an important problem in empirical
sciences. In the last few years, there has been a considerable interest in
adapting machine learning algorithms to the problem of estimating heterogeneous
effects from observational and experimental data. However, these algorithms
often make strong assumptions about the observed features in the data and
ignore the structure of the underlying causal model, which can lead to biased
estimation. At the same time, the underlying causal mechanism is rarely known
in real-world datasets, making it hard to take it into consideration. In this
work, we provide a survey of state-of-the-art data-driven methods for
heterogeneous treatment effect estimation using machine learning, broadly
categorizing them as methods that focus on counterfactual prediction and
methods that directly estimate the causal effect. We also provide an overview
of a third category of methods which rely on structural causal models and learn
the model structure from data. Our empirical evaluation under various
underlying structural model mechanisms shows the advantages and deficiencies of
existing estimators and of the metrics for measuring their performance.

本文综述了数据驱动方法在异质性治疗效应估计上的应用，分为基于反事实预测和直接估计因果效应的方法，还有从数据中学习模型结构的结构因果模型方法，并在不同结构模型机制下进行了实证评估。

基于数据驱动的异质性治疗效应估计

Data-Driven Estimation of Heterogeneous Treatment Effects

Estimating how a treatment affects units individually, known as heterogeneous
treatment effect (HTE) estimation, is an essential part of decision-making and
policy implementation. The accumulation of large amounts of data in many
domains, such as healthcare and e-commerce, has led to increased interest in
developing data-driven algorithms for estimating heterogeneous effects from
observational and experimental data. However, these methods often make strong
assumptions about the observed features and ignore the underlying causal model
structure, which can lead to biased HTE estimation. At the same time,
accounting for the causal structure of real-world data is rarely trivial since
the causal mechanisms that gave rise to the data are typically unknown. To
address this problem, we develop a feature selection method that considers each
feature's value for HTE estimation and learns the relevant parts of the causal
structure from data. We provide strong empirical evidence that our method
improves existing data-driven HTE estimation methods under arbitrary underlying
causal structures. Our results on synthetic, semi-synthetic, and real-world
datasets show that our feature selection algorithm leads to lower HTE
estimation error.

该研究通过开发一种考虑每个特征值对异质性治疗效果估计的特征选择方法，并从数据中学习相关部分的因果结构，为处理真实世界数据的因果结构问题提供了解决方法。研究结果表明，该方法在任意潜在因果结构下均优于现有的数据驱动异质性治疗效果估计方法，并可降低异质性治疗效果估计误差。

在结构不确定性下改进基于数据驱动的异质性处理效应估计

Improving Data-driven Heterogeneous Treatment Effect Estimation Under  Structure Uncertainty

The causal effect of a treatment can vary from person to person based on
their individual characteristics and predispositions. Mining for patterns of
individual-level effect differences, a problem known as heterogeneous treatment
effect estimation, has many important applications, from precision medicine to
recommender systems. In this paper we define and study a variant of this
problem in which an individual-level threshold in treatment needs to be
reached, in order to trigger an effect. One of the main contributions of our
work is that we do not only estimate heterogeneous treatment effects with fixed
treatments but can also prescribe individualized treatments. We propose a
tree-based learning method to find the heterogeneity in the treatment effects.
Our experimental results on multiple datasets show that our approach can learn
the triggers better than existing approaches.

本文探讨了异质性治疗效应估计的一个变体问题，提出了一种基于树形学习方法来寻找治疗效应异质性的方法，可以学习和确认个体化的治疗方案，并在多个数据集上的实验证明了该方法比现有方法更为优越。