A further understanding of cause and effect within observational data is critical across many domains, such as economics, health care, public policy, web mining, online advertising, and marketing campaigns. Although significant advances have been made to overcome the challenges in causal effect estimation with observational data, such as missing counterfactual outcomes and selection bias between treatment and control groups, the existing methods mainly focus on source-specific and stationary observational data. Such learning strategies assume that all observational data are already available during the training phase and from only one source. This practical concern of accessibility is ubiquitous in various academic and industrial applications. That's what it boiled down to: in the era of big data, we face new challenges in causal inference with observational data, i.e., the extensibility for incrementally available observational data, the adaptability for extra domain adaptation problem except for the imbalance between treatment and control groups, and the accessibility for an enormous amount of data. In this position paper, we formally define the problem of continual treatment effect estimation, describe its research challenges, and then present possible solutions to this problem. Moreover, we will discuss future research directions on this topic.
翻译:在许多领域,如经济学、医疗保健、公共政策、网络挖掘、在线广告和营销活动中,进一步理解观察数据中的因果关系至关重要。虽然在观察数据中的因果效应估计方面已经取得了重大进展,如缺失对照结果和治疗组与对照组之间的选择偏差,但现有方法主要集中在特定来源和稳态观测数据上。这些学习策略假设在训练阶段已经拥有所有的可观察数据,并且来自一个数据来源。这种可访问性的实际问题在各种学术和工业应用中普遍存在。在大数据时代,我们面临着利用观察数据进行因果推断的新挑战,即对增量可用的观察数据进行扩展性、除治疗和对照组之间不平衡外还要适应额外的领域适应问题以及处理大量数据的可访问性。在本文中,我们正式定义了持续处理效应估计的问题,描述了其研究挑战,并提出了可能的解决方案。此外,我们还将讨论这一主题的未来研究方向。