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Article overview
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Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Survey | | Debo Cheng
; Jiuyong Li
; Lin Liu
; Jixue Liu
; Thuc Duy Le
; | | Date: |
20 Aug 2022 | | Abstract: | 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. | | Source: | arXiv, 2208.09590 | | Services: | Forum | Review | PDF | Favorites | |
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