Approximating Counterfactual Bounds while Fusing Observational, Biased and Randomised Data Sources

Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas, David Huber
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引用次数: 1

Abstract

We address the problem of integrating data from multiple, possibly biased, observational and interventional studies, to eventually compute counterfactuals in structural causal models. We start from the case of a single observational dataset affected by a selection bias. We show that the likelihood of the available data has no local maxima. This enables us to use the causal expectation-maximisation scheme to approximate the bounds for partially identifiable counterfactual queries, which are the focus of this paper. We then show how the same approach can address the general case of multiple datasets, no matter whether interventional or observational, biased or unbiased, by remapping it into the former one via graphical transformations. Systematic numerical experiments and a case study on palliative care show the effectiveness of our approach, while hinting at the benefits of fusing heterogeneous data sources to get informative outcomes in case of partial identifiability.
在融合观测、有偏和随机数据源的同时逼近反事实界限
我们解决了从多个可能有偏差的观察性和干涉性研究中整合数据的问题,以最终计算结构因果模型中的反事实。我们从受选择偏差影响的单个观测数据集开始。我们证明了可用数据的似然没有局部最大值。这使我们能够使用因果期望最大化方案来近似部分可识别的反事实查询的边界,这是本文的重点。然后,我们展示了相同的方法如何解决多个数据集的一般情况,无论是干预性的还是观察性的,有偏见的还是无偏的,通过图形转换将其重新映射到前一个数据集。系统的数值实验和姑息治疗的案例研究表明了我们的方法的有效性,同时暗示了在部分可识别的情况下融合异构数据源以获得信息结果的好处。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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