论文标题
部分可观测时空混沌系统的无模型预测
Bayesian Counterfactual Mean Embeddings and Off-Policy Evaluation
论文作者
论文摘要
反事实分布对未处理组中治疗的效果进行了建模。尽管大多数工作都集中在治疗效果的预期值上，但人们可能对整个反事实分布或与之相关的其他数量感兴趣。在贝叶斯条件平均嵌入框架的框架内，我们提出了一种贝叶斯的方法来建模反事实分布，从而导致量化有关分布的认知不确定性。该框架自然扩展到观察多种治疗效果的环境（例如，临时临时效应，最终的治疗效应是主要感兴趣的），并允许对这些效果的关系进行额外的建模。对于这样的目标，我们提出了三种新型的贝叶斯方法来估计最终治疗效应的期望，仅当提供了中间效应和最终效应之间依赖性的嘈杂样本时。这些方法在考虑的不确定性来源上有所不同，并允许将两个数据源组合在一起。此外，我们将这些想法概括为非政策评估框架，这可以看作是反事实估计问题的扩展。我们从经验上探索了需要数据融合的两个不同实验设置中算法的校准，并说明了考虑来自两个数据源的不确定性的价值。
The counterfactual distribution models the effect of the treatment in the untreated group. While most of the work focuses on the expected values of the treatment effect, one may be interested in the whole counterfactual distribution or other quantities associated to it. Building on the framework of Bayesian conditional mean embeddings, we propose a Bayesian approach for modeling the counterfactual distribution, which leads to quantifying the epistemic uncertainty about the distribution. The framework naturally extends to the setting where one observes multiple treatment effects (e.g. an intermediate effect after an interim period, and an ultimate treatment effect which is of main interest) and allows for additionally modelling uncertainty about the relationship of these effects. For such goal, we present three novel Bayesian methods to estimate the expectation of the ultimate treatment effect, when only noisy samples of the dependence between intermediate and ultimate effects are provided. These methods differ on the source of uncertainty considered and allow for combining two sources of data. Moreover, we generalize these ideas to the off-policy evaluation framework, which can be seen as an extension of the counterfactual estimation problem. We empirically explore the calibration of the algorithms in two different experimental settings which require data fusion, and illustrate the value of considering the uncertainty stemming from the two sources of data.
