## 3 Data Bias
### Selection Bias
With regard to the health domain, an additional five can be found in the literature. In [45], the selection bias that can be found in electronic health records (i.e., EHRs), which is caused due to missing values can be reduced by implementing an inverse probability weighting (i.e., IPW) adjustment technique. Moreover, the authors utilized chained equations to fill any additional missing features, showcasing that data-cleaning techniques can also assist in mitigating data bias. Data cleaning is also used in [46] to address selection bias. A threshold of 30% is set which means that if a feature has more than 30% missing values, it should be eliminated. The goal of this procedure is to ensure that every feature consists of the approximately same number of missing values. Moreover, since the data come from different hospitals of different sizes, the dataset is clipped in order to contain an equal number of records from every hospital, thus avoiding the domination of large-sized hospitals. Similarly, in [47] a clique-based causal feature separation (i.e., CCFS) algorithm is presented that theoretically guarantees that either the largest clique or the rest of the causal skeleton is the exact set of all causal features of the outcome. In [48], the authors address the issue of medical question-answering (i.e., Q&A) and propose label resetting in order to avoid the effects of selection bias in medical text data, simultaneously preserving the textual content and the corresponding semantic information. In another health-related approach [49], four propensity methods are presented and tested, including one-to-one matching with replacement, IPW and two methods based on doubly robust (i.e., DR) estimation that combines outcome regression and IPW. In [50], IPW is also being used and a two-stage process if followed, consisting of a classification and a regression model that uses the reweighted results of the classification. In essence, this procedure enhances underrepresented records in the dataset and reduces the weights of overrepresented records. A group fairness metric for model selection called area delimited between classification (i.e., ABC) is introduced in [51] that is based on item response theory (i.e., IRT) and differential item functioning (i.e., DIF) characteristic curve and aims to identify selection bias, also showcasing promising results. Apart from the above-mentioned methods, the authors in [52] introduce Imitate (Identify and Mitigate Selection Bias) for mitigating selection bias, which they have also released as a Python module [53]. The idea behind this approach is that the algorithm first calculates the dataset's probability density and then adds generated points to smooth out the density. If the points are concentrated in certain areas and are not widespread, this could be an indication that selection bias is present in the dataset. However, the Imitate algorithm assumes that there exists only one normally distributed cluster per class in the dataset. To this end, the authors in [54] propose MIMIC (MultiIMtate Bias Correction), which utilizes Imitate as a building block, being, however, capable of handling multi-cluster datasets by speculating that the ground-truth data consists of a set of multivariate Gaussians. Even though this is a limiting assumption, MIMIC is able to deal with more datasets than Imitate. In another approach [55], the authors discuss selection bias in recommender systems, which is a common issue in such systems, since they heavily rely on users' historical data [56]. Their proposed approach consists of a data-filling strategy utilizing, irrelevant to the users, items based on temporal visibility. A complete list of approaches that have been proposed to identify and/or mitigate selection bias can be found in Table 4.