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based on statistical correlation and half-sibling regression, which leverages the statistical dependency between
gender-biased word vectors and gender-definition word vectors to learn the counterfactual gender informa-
tion of an individual through causal inference. The learned spurious gender information is then subtracted
from the gender-biased word vectors to remove the gender bias. Lu et al. [80] proposed a method called CDA to
eliminate gender bias through counterfactual data augmentation. The main idea of CDA is to augment the cor-
pus by exchanging gender word pairs in the corpus and constructing matching gender word pairs with causal
interventions. As such, CDA breaks associations between gendered and gender-neutral words and alleviates
the problem that gender bias increases as loss decreases when training with gradient descent.

There exists a certain degree of bias and fairness issues in word embedding, machine translation, sentiment
analysis, language models, and dialog generation in NLP. At present, most studies only focus on a single bias
(such as gender bias), and there is a lack of research results on other biases or eliminating multiple biases at the
same time. Therefore, how should we analyze and evaluate the mechanism and impact of multi-bias in word
embedding and machine learning algorithms? Establishing effective techniques for eliminating various biases
in word embedding and machine learning algorithms requires further research which needs to be carried out
for fair NLP.

6.4. Fair medical
Electronic health records (EHRe) contain large amounts of clinical information about heterogeneous patients
and their responses to treatments. It is possible for machine learning techniques to efficiently leverage the full
extent of EHRs to help physicians make predictions for patients, thus greatly improving the quality of care
and reducing costs. However, because of discrimination implicitly embedded in EHRs, the automated systems
may introduce or even aggravate the nursing gap between underrepresented groups and disadvantaged ones.
The prior works on eliminating discrimination for clinical predictive models mostly focus on statistics-based
fairness-enhancing approaches [81,82] . In addition, they do not provide an effective evaluation of fairness to
individuals, and the fairness metrics they used are difficult to verify. Some recent studies focus on assessing
fairness for clinical predictive models from a causal perspective [83,84] . For example, Pfohl et al. [83] proposed a
counterfactual fairness notion to extend fairness to the individual level and leveraged variational autoencoder
technology to eliminate discrimination against certain patients.

6.5. Causal analysis packages
This section introduces some representative packages or software for causal analysis, which are helpful for us
to develop causality-based fairness-enhancing approaches. These packages can be roughly divided into two
categories: one for discovering potential causal structure in data and the other for making causal inferences.
Table 2 summarizes typical packages or software for causal analysis.

TETRAD [85] is a full-featured software for causal analysis after considerable development where it can be used
to discover the causal structure behind the dataset, estimate the causal effects, simulate the causal models,
etc. TETRAD can accept different types of data as input, e.g., discrete data, continuous data, time series data,
etc. The users can choose the appropriate well-tested causal discovery algorithms it integrates to search causal
structure, as well as input prior causal knowledge to limit the search. In addition, TETRAD can parameterize
the causal model and simulate the data according to the existing causal diagram. Causal-learn package [86] is
the Python version of TETRAD. It provides the implementation of the latest causal discovery methods rang-
ing from constraint-based methods, score-based methods, and constrained functional causal models-based
methods to permutation-based methods. In addition, there are many packages for causal discovery [87–89] .
Tigramite [88] focuses on searching causal structure from observational time series data. In addition to provid-
ing classic causal discovery algorithms, gCastle [89] provides many gradient-based causal discovery approaches.

CausalML [90] is a Python package which encapsulates many causal learning and causal inference approaches.