A Comparison of Some Predictive Models for Modeling Abortion Rate in Russia

Predictive modeling techniques are popular methods for building models to predict a target of interest. In many modeling problems, however, the focus is to identify possible factors that have significant association with the target. For this type of problem, it is very easy to stretch the interpretation of an association relationship to a causation relationship. Practitioners must pay special attention to such a misinterpretation when data are observational data. In addition, the process of data collection and cleansing are critical in order to produce quality data for modeling. In this article, an observational study is conducted to illustrate the issues about data quality and model building to identify potential important factors associated with abortion rate using data collected in Russia from year 2000 to 2009. Some pitfalls and cautions of applying predictive modeling techniques are discussed.