In many fields of scientific research and real-world applications, unbiased estimation of causal effects from non-experimental data is crucial for understanding the mechanism underlying the data and for decision-making on effective responses or interventions. A great deal of research has been conducted on this challenging problem from different angles. For causal effect estimation in data, assumptions such as Markov property, faithfulness and causal sufficiency are always made. Under the assumptions, full knowledge such as, a set of covariates or an underlying causal graph, is still required. A practical challenge is that in many applications, no such full knowledge or only some partial knowledge is available. In recent years, research has emerged to use a search strategy based on graphical causal modelling to discover useful knowledge from data for causal effect estimation, with some mild assumptions, and has shown promose in tackling the practical challenge. In this survey, we review the methods and focus on the challenges the data-driven methods face. We discuss the assumptions, strengths and limitations of the data-driven methods. We hope this review will motivate more researchers to design better data-driven methods based on graphical causal modelling for the challenging problem of causal effect estimation.

本文综述了通过基于图形因果模型的搜索策略，发现因果效应相关有用知识的数据驱动方法，重点讨论这些方法面临的挑战，探讨了方法的假设、优点和局限性，希望这篇文章能够激励更多的研究人员设计更好的数据驱动方法来解决因果效应估计等领域的实际问题。

基于图形因果模型的数据驱动因果效应估计: 一项调查