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(a) (b)

Figure 9. Statistical charts of references in this survey: (a) a pie chart that shows the proportion of publications in journals or conferences;
and (b) a bar chart about the number of publications per year (from 2017 to 2021).

direction in the near future.

Build a more complete ecosystem. There is an interaction of fairness between applications in the real world.
For example, in bank loans, there exists discrimination in loan quotas for groups of different genders, and this
unfairness may be caused by the salary level of groups of different genders in the workplace. Therefore, we
need to further explore achieving cross-domain, cross-institution collaborative fairness algorithms.

8. CONCLUSION
This review presents the relevant background, typical causality-based fairness notions, an inclusive overview
of causality-based fairness methods, and their applications. The challenges of applying causality-based fairness
notions in practical scenarios and future research trends on solving fairness problems in algorithms are also
discussed.

Papers related to the topic of addressing fairness issues based on causality are mostly reviewed in this survey.
The statistics and analysis of these papers are also carried out in this survey and the results are presented in
Figure 9. Figure 9(a) reports the proportion of papers published in reputable journals or conferences, while
Figure 9(b) shows the number of publications from 2017 to 2021. The research community has only recently
focused on defining, measuring, and mitigating discrimination in algorithms from a causal perspective, grad-
ually realizing the importance of causal modeling of the problems to address fairness issues.

Therefore, we provide a relatively complete review of causality-based fairness-enhancing techniques to help
researchers gain a deep understanding of this field, and we hope that more researchers will engage in this
young but important field. On the one hand, discrimination detection and elimination from the causal per-
spectiverather than statistics-based methods is more welcomed and trusted by the users of automated decision
making systems, since causality-based fairness-enhancing methods consider how the data are generated and
thus deeply understand the sources of discrimination. On the other hand, because of the completeness of the
causal theory, it provides mathematical tools to discover discrimination when the dataset includes bias due to
missing data. In addition, the main objective of this survey is to bridge the gap between the practical scenarios
of discrimination elimination from the causal perspective and the ongoing theory problem. This is achieved
by summing up causality-based fairness notions, approaches, and their limitations. Although the causal graph
cannot be constructed without some untestable assumptions, it can still be used productively as well as serve