Causes of Effects: Learning individual responses from population data
Causes of Effects: Learning individual responses from population data
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影响原因：从人口数据中了解个体反应
DOI：
10.24963/ijcai.2022/376
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
J. Pearl
中科院分区：
文献类型：
--
作者：
Scott Mueller;Ang Li;J. Pearl
个性化的问题在几乎每个科学领域都至关重要。确定特定观察到的事件的原因对于准确的决策和解释也至关重要。但是，这些任务调用了反事实关系，因此从人口数据中不确定。例如，从治疗中受益的可能性涉及一个人，如果接受治疗，结果有利，如果没有治疗，则结果不利；即使以细粒度的特征为条件，我们也无法从实验数据中估算出来，因为我们不能为个人测试这两种可能性。田和珍珠使用实验和观察数据的组合就因果关系和其他因果概率提供了界限。当以因果模型的形式获得结构信息时，这些界限虽然很紧，但可以显着缩小。这些添加的信息可能会提供解决中心问题的权力，例如可解释的AI，法律责任和个性化医学，所有这些都要求反事实逻辑。本文得出，分析和表征了这些新界限，并说明了它们的一些实际应用。
The problem of individualization is crucial in almost every field of science. Identifying causes of specific observed events is likewise essential for accurate decision making as well as explanation. However, such tasks invoke counterfactual relationships, and are therefore indeterminable from population data. For example, the probability of benefiting from a treatment concerns an individual having a favorable outcome if treated and an unfavorable outcome if untreated; it cannot be estimated from experimental data, even when conditioned on fine-grained features, because we cannot test both possibilities for an individual. Tian and Pearl provided bounds on this and other probabilities of causation using a combination of experimental and observational data. Those bounds, though tight, can be narrowed significantly when structural information is available in the form of a causal model. This added information may provide the power to solve central problems, such as explainable AI, legal responsibility, and personalized medicine, all of which demand counterfactual logic. This paper derives, analyzes, and characterizes these new bounds, and illustrates some of their practical applications.
使用因果图选择单位
DOI：
--
发表时间：
2022
期刊：
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22
影响因子：
--
作者：
J. Pearl
通讯作者：
J. Pearl
基于反事实逻辑的单元选择
DOI：
10.24963/ijcai.2019/248
发表时间：
2019
期刊：
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
影响因子：
--
作者：
Li, Ang;Pearl, Judea
通讯作者：
Pearl, Judea