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Necessity for causality
Causality model Group-based notions
Intervention Individual-based notions Pre-processing
Concepts for causal models In-processing
Post-processing Data missing
Causality-based fairness notions Fair NLP Causal discovery
Fair medical
Identifiable issues
Classic causality-based fairness methods Fair recommender Systems Comprehensive definition
Dynamic system
Causal analysis packages
Other challenges
Applications of fair machine learning Furture trends
Challenges and furture trends
Figure 1. Organizational structure of this paper.

the observational data without extra assumptions.

The previous survey articles offer a high-level summary of technology to eliminate algorithm discrimination,
and there is no detailed discussion on specific subareas [16–19] . Wan et al. [20] provided an exhaustive review of
the methods of using the in-process mechanism to solve the fairness issues. Makhlouf et al. [21] summarized
the advantages and disadvantages of each fairness notion and their suitability, aiming to help us select the
fairness notions that are most suitable for a particular scenario. Makhlouf et al. [22] only focused on reviewing
the concept of fairness based on causality. Instead of eliminating discrimination from a technical perspective,
Wu et al. [23] summarized human-oriented fairness-enhancing methods as a way to explore the role of human
beings in addressing algorithmic fairness.

Tocomplementtheprevioussurveypapersonfairness-enhancingmachinelearning, this surveythoroughlyre-
views the concept of fairness based on causality and summarizes the core ideology behind the causality-based
fairness-enhancing approaches. This survey aims to stimulate future exploration of causality-based fairness
technology, because of the importance of causal modeling in improving algorithmic fairness. In this survey,
the review of the concept and technology of fairness based on causality is conducted in several phases. The
surveyfirstreviewscausality-baseddefinitionsandmeasuresoffairnessandsummarizesthesuitabilityofthese
causality-based fairness notions. Next, it provides a comprehensive overview of state-of-the-art methods to
achieve fairness based on these causality-based fairness notions. The survey also discusses the practical appli-
cations, beyond classification, that the causality-based fairness methods are expected to benefit greatly. Finally,
this survey discusses the challenges of eliminating discrimination from a causal perspective, including the ac-
quisition of causal graphs and identifiable issues. It also reviews the efforts for addressing these challenges and
summarizes the remaining issues, which provides some assistance to solve these problems for future research.
Figure 1 shows the organizational structure of this survey.

The rest of this survey is structured as follows. Section 2 introduces an example to interpret the importance
of causal modeling for addressing fairness issues. Section 3 presents the background of the causal model. Sec-
tion 4 introduces definitions and measures of causality-based fairness notions and discusses the suitability or
applicability of them. Section 5 discusses fairness mechanisms and causality-based fairness approaches, and
comparesthesemechanisms. Section6introducesseveraltypicalapplicationsofcausality-basedfairnessmeth-
ods. Section 7 analyzes the challenges and the research trends for applying causality-based fairness-enhancing
methods in practical scenarios.

2. THE IMPORTANCE FOR CAUSALITY TO DETECT DISCRIMINATION: AN EXAMPLE
The importance of applying causal analysis to discrimination discovery is explored in this section. Consider
a simple example that is inspired by a legal dispute about religious discrimination in recruitment [24] . To keep
the situation simple, assuming that a company takes the religious belief ( = 1 if an applicant has a religious