使用 R 包 cfid 识别反事实查询
Identifying Counterfactual Queries with the R Package cfid
AI总结：
本文介绍了 R 包 cfid，它实现了 Shpitser 和 Pearl 提出的 ID* 和 IDC* 算法，用于从观察和实验数据中识别结构因果模型框架下的反事实查询和条件反事实查询。
摘要：
在结构因果模型框架中，反事实查询描述了涉及所研究系统多个替代状态的事件。反事实查询通常采取“如果……会怎样”类型问题的形式，例如“如果一名申请者有超过10年的经验，而实际上他们只有5年的经验，他们会被录用吗？”这类问题以及一般的反事实推断至关重要，例如在解决决策公平性问题时。由于反事实事件包含矛盾的世界状态，如果不做出几个限制性假设，就不可能进行随机实验来解决它们。然而，有时可以通过将所研究的系统表示为因果模型，将可用数据表示为符号概率分布，从观察和实验数据中识别此类查询。Shpitser 和 Pearl (2007) 构建了两种算法，分别称为 ID* 和 IDC*，用于识别反事实查询和条件反事实查询。这两种算法类似于 Shpitser 和 Pearl (2006) 用于识别干预分布的 ID 和 IDC 算法，后者由 Tikka 和 Karvanen (2017) 在 causaleffect 包中用 R 语言实现。我们展示了实现 ID* 和 IDC* 算法的 R 包 cfid。通过示例演示了反事实查询的识别和 cfid 的功能。
英文摘要：
In the framework of structural causal models, counterfactual queries describe events that concern multiple alternative states of the system under study. Counterfactual queries often take the form of "what if" type questions such as "would an applicant have been hired if they had over 10 years of experience, when in reality they only had 5 years of experience?" Such questions and counterfactual inference in general are crucial, for example when addressing the problem of fairness in decision-making. Because counterfactual events contain contradictory states of the world, it is impossible to conduct a randomized experiment to address them without making several restrictive assumptions. However, it is sometimes possible to identify such queries from observational and experimental data by representing the system under study as a causal model, and the available data as symbolic probability distributions. Shpitser and Pearl (2007) constructed two algorithms, called ID* and IDC*, for identifying counterfactual queries and conditional counterfactual queries, respectively. These two algorithms are analogous to the ID and IDC algorithms by Shpitser and Pearl (2006) for identification of interventional distributions, which were implemented in R by Tikka and Karvanen (2017) in the causaleffect package. We present the R package cfid that implements the ID* and IDC* algorithms. Identification of counterfactual queries and the features of cfid are demonstrated via examples.
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