  Missing values are a major challenge in most data science projects working on
real data. To avoid losing valuable information, imputation methods are used to
fill in missing values with estimates, allowing the preservation of samples or
variables that would otherwise be discarded. However, if the process is not
well controlled, imputation can generate spurious values that introduce
uncertainty and bias into the learning process. The abundance of univariate and
multivariate imputation techniques, along with the complex trade-off between
data reliability and preservation, makes it difficult to determine the best
course of action to tackle missing values. In this work, we present ITI-IQA
(Imputation Quality Assessment), a set of utilities designed to assess the
reliability of various imputation methods, select the best imputer for any
feature or group of features, and filter out features that do not meet quality
criteria. Statistical tests are conducted to evaluate the suitability of every
tested imputer, ensuring that no new biases are introduced during the
imputation phase. The result is a trainable pipeline of filters and imputation
methods that streamlines the process of dealing with missing data, supporting
different data types: continuous, discrete, binary, and categorical. The
toolbox also includes a suite of diagnosing methods and graphical tools to
check measurements and results during and after handling missing data.
