论文标题
使用Polle软件包学习政策
Policy Learning with the polle package
论文作者
论文摘要
R软件包Polle是基于观察数据的有限阶段政策学习和评估有限阶段政策的统一框架。该软件包实现了因果政策学习的现有和新颖方法的集合，包括双重限制的Q学习，策略树学习和成果加权学习。该包仅通过考虑现实的政策来处理（近）违反阳性的行为。高度灵活的机器学习方法可用于估计令人讨厌的组件，并通过交叉拟合确保策略值的有效推断。该库是围绕一个简单的语法构建的，该语法具有四个主要函数polition_data（），polition_def（），polition_learn（）和用于指定数据结构的polition_eval（），定义用户指定的策略，指定策略学习方法并评估（学习）策略。包装的功能通过广泛可重复的示例说明。
The R package polle is a unifying framework for learning and evaluating finite stage policies based on observational data. The package implements a collection of existing and novel methods for causal policy learning including doubly robust restricted Q-learning, policy tree learning, and outcome weighted learning. The package deals with (near) positivity violations by only considering realistic policies. Highly flexible machine learning methods can be used to estimate the nuisance components and valid inference for the policy value is ensured via cross-fitting. The library is built up around a simple syntax with four main functions policy_data(), policy_def(), policy_learn(), and policy_eval() used to specify the data structure, define user-specified policies, specify policy learning methods and evaluate (learned) policies. The functionality of the package is illustrated via extensive reproducible examples.
