Estimating personalized effects of treatments is a complex, yet pervasive
problem. To tackle it, recent developments in the machine learning (ML)
literature on heterogeneous treatment effect estimation gave rise to many
sophisticated, but opaque, tools: due to their flexibility, modularity and
ability to learn constrained representations, neural networks in particular
have become central to this literature. Unfortunately, the assets of such black
boxes come at a cost: models typically involve countless nontrivial operations,
making it difficult to understand what they have learned. Yet, understanding
these models can be crucial -- in a medical context, for example, discovered
knowledge on treatment effect heterogeneity could inform treatment prescription
in clinical practice. In this work, we therefore use post-hoc feature
importance methods to identify features that influence the model's predictions.
This allows us to evaluate treatment effect estimators along a new and
important dimension that has been overlooked in previous work: We construct a
benchmarking environment to empirically investigate the ability of personalized
treatment effect models to identify predictive covariates -- covariates that
determine differential responses to treatment. Our benchmarking environment
then enables us to provide new insight into the strengths and weaknesses of
different types of treatment effects models as we modulate different challenges
specific to treatment effect estimation -- e.g. the ratio of prognostic to
predictive information, the possible nonlinearity of potential outcomes and the
presence and type of confounding.

研究利用后处理的特征重要性方法来评估治疗效果估计器，通过建立基准环境，从而以往未被重视的新维度来研究个性化治疗效果模型的能力，以识别影响模型预测的特征，并提供有关处理效果模型的不同类型的优缺点的新见解。

通过解释性视角评估异质性处理效果模型的基准性能

Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability

Accurately estimating personalized treatment effects within a study site
(e.g., a hospital) has been challenging due to limited sample size.
Furthermore, privacy considerations and lack of resources prevent a site from
leveraging subject-level data from other sites. We propose a tree-based model
averaging approach to improve the estimation accuracy of conditional average
treatment effects (CATE) at a target site by leveraging models derived from
other potentially heterogeneous sites, without them sharing subject-level data.
To our best knowledge, there is no established model averaging approach for
distributed data with a focus on improving the estimation of treatment effects.
Specifically, under distributed data networks, our framework provides an
interpretable tree-based ensemble of CATE estimators that joins models across
study sites, while actively modeling the heterogeneity in data sources through
site partitioning. The performance of this approach is demonstrated by a
real-world study of the causal effects of oxygen therapy on hospital survival
rate and backed up by comprehensive simulation results.

本研究提出了一种基于树模型进行模型平均的方法，旨在提高分布式数据网络下个性化治疗效果 (CATE) 估计精度，包括通过站点划分与模型组合来建模数据源异质性等。该方法通过真实世界数据的研究和全面的仿真实验表明其性能和提高治疗效果估计的实用价值。