Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effect
Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effect
复制标题
用于识别治疗效果增强的亚组的因果规则集
DOI：
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复制
发表时间：
2017
影响因子：
2.1
通讯作者：
C. Rudin
中科院分区：
文献类型：
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作者：
Tong Wang;C. Rudin
因果推断分析中的一个关键问题是如何找到治疗效果提高的亚组。本文采用机器学习的方法，并介绍了一个生成模型，因果规则集（CRS），可解释的子群发现。CRS模型使用一小组简短的决策规则来捕获平均治疗效果提高的亚组。我们提出了一个贝叶斯框架学习因果规则集。贝叶斯模型由一个先验模型和一个贝叶斯逻辑回归模型组成，前者支持简单模型以获得更好的可解释性，并避免过度拟合，后者捕获数据的可能性，表征结果，属性和子组成员之间的关系。贝叶斯模型具有可调参数，可以表征不同大小的亚组，为用户提供更灵活的治疗有效边界模型选择。我们在规则集和参数的联合解空间中使用迭代离散蒙特卡罗步骤找到最大后验模型。为了提高搜索效率，我们提供了理论基础的算法和边界策略来修剪和限制搜索空间。实验表明，该搜索算法可以有效地恢复真实的底层子组。我们将CRS应用于公共和真实世界的数据集，这些数据集来自可解释性不可或缺的领域。我们比较CRS与最先进的基于规则的子群发现模型。结果表明，CRS在来自各个领域的数据集上实现了一致的竞争性能，以高治疗效率前沿为代表。贡献摘要：本文的动机是在许多应用程序中的治疗效果的巨大异质性和需要准确地定位增强治疗效果的亚组。现有的方法要么依赖于先验假设来发现子组，要么依赖于贪婪方法，例如基于树的递归划分。我们的方法采用机器学习方法来找到一个最优的子群学习一个仔细的全局目标。与基于树的基线相比，我们的模型通过使用一组短决策规则在捕获子组方面更灵活。我们使用一种新型指标（治疗效率边界）来评估我们的模型，该指标表征了亚组规模和可实现的治疗效果之间的权衡，并且我们的模型表现出比基线模型更好的性能。
A key question in causal inference analyses is how to find subgroups with elevated treatment effects. This paper takes a machine learning approach and introduces a generative model, causal rule sets (CRS), for interpretable subgroup discovery. A CRS model uses a small set of short decision rules to capture a subgroup in which the average treatment effect is elevated. We present a Bayesian framework for learning a causal rule set. The Bayesian model consists of a prior that favors simple models for better interpretability as well as avoiding overfitting and a Bayesian logistic regression that captures the likelihood of data, characterizing the relation between outcomes, attributes, and subgroup membership. The Bayesian model has tunable parameters that can characterize subgroups with various sizes, providing users with more flexible choices of models from the treatment-efficient frontier. We find maximum a posteriori models using iterative discrete Monte Carlo steps in the joint solution space of rules sets and parameters. To improve search efficiency, we provide theoretically grounded heuristics and bounding strategies to prune and confine the search space. Experiments show that the search algorithm can efficiently recover true underlying subgroups. We apply CRS on public and real-world data sets from domains in which interpretability is indispensable. We compare CRS with state-of-the-art rule-based subgroup discovery models. Results show that CRS achieves consistently competitive performance on data sets from various domains, represented by high treatment-efficient frontiers. Summary of Contribution: This paper is motivated by the large heterogeneity of treatment effect in many applications and the need to accurately locate subgroups for enhanced treatment effect. Existing methods either rely on prior hypotheses to discover subgroups or greedy methods, such as tree-based recursive partitioning. Our method adopts a machine learning approach to find an optimal subgroup learned with a carefully global objective. Our model is more flexible in capturing subgroups by using a set of short decision rules compared with tree-based baselines. We evaluate our model using a novel metric, treatment-efficient frontier, that characterizes the trade-off between the subgroup size and achievable treatment effect, and our model demonstrates better performance than baseline models.
用于可解释的个体化治疗效果估计的自适应超框匹配
DOI：
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发表时间：
2020
期刊：
Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI
影响因子：
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作者：
Morucci, Marco;Orlandi, Vittorio;Roy, Sudeepa;Rudin, Cynthia;Volfovsky, Alexander
通讯作者：
Volfovsky, Alexander
DOI：
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发表时间：
2015
期刊：
--
影响因子：
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作者：
Raj Chetty;Nathaniel Hendren;Lawrence Katz
通讯作者：
Raj Chetty;Nathaniel Hendren;Lawrence Katz
DOI：
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发表时间：
2017-07
期刊：
J. Mach. Learn. Res.
影响因子：
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作者：
Sudeepa Roy;C. Rudin;A. Volfovsky;Tianyu Wang-
通讯作者：
Sudeepa Roy;C. Rudin;A. Volfovsky;Tianyu Wang-