Confounding variable is a statistical term.The concept is a bit confusing for many people because of the method to use. For starters, different researchers have different explanations for confounding variables. Even though the definition is the same, the research context is moderately specific to the field. Therefore, experts in different industries apply this technique for solutions in unique ways. So before explaining your take on confounding variables, it’s important to understand the other person’s implication of the term. Thus, this article includes valuable information confounding variables in machine learning.
A confounding variable is an external influence in an experiment. In other words, these variables affect the output of the model by manipulating dependent and independent variables. Subsequently, confounding variables act on dependent variables, leading to inaccurate results.
In the course of correlational research, these variables greatly impact the superficial relationship concerning two variables. It defines whether dependent and independent variables change to zero, negative, or positive value. You can also refer to confounding variables as a factor that a researcher cannot remove or control, even though it changes the model’s validity.
Confounding in Machine Learning
Previously, confounding variables agitated the outcomes in applied statistics. In view of statistics, the research depends on independent variables’ relationship with dependent variables in data. Researchers resolve confounding variables and improve relationships for the outcome through statistical methods. They design these techniques to invalidate or corrupt discoveries.
Machine learning practitioners are concerned about improving the predictive model’s capabilities instead of statistical interpretability and correctness. Nevertheless, confounding variables are the focus of attention while selecting and preparing the data. But while developing the descriptive statistical models, these variables are less important. Still, experts in applied machine learning consider the confounding variable to be critically essential.
Data scientists experiment with dependent and independent variables to evaluate the machine learning model. Mainly, the focus of these experiments is to minimize the confounding variable and its influence on the results.
Impact of Assessing Machine Learning Model
If you know about applied machine learning, it may be surprising for you as gold-standard practices include confounding variables. Machine learning experiments for confounding variables include choosing and interpreting techniques for evaluating the machine learning model. It is essential to consider the impact of variables while assessing the model and identifying independent variables. Here are some choices impacting dependent variables throughout the experiment:
- Preparing the data schemes,
- Learning algorithm,
- Configuring learning algorithm,
- Initializing learning algorithm,
- A sampling of the training dataset
- A sampling of the test dataset.
Hence, you can choose these metrics while evaluating the model’s ability to generate exact predictions. Considering the evaluation of the machine learning model, designing and executing the controlled experiments will be favorable. In a controlled experiment, the model isolates other variables and focuses on a single element. The two common controlled experiments types are:
- Evaluation of learning algorithm
- Evaluation of learning algorithm configurations
Randomization in Machine Learning
Controlled experiments cannot hold all the confounding variables constant. Hence, there are sources of randomness indicating that if the experiment holds these variables constant, the model’s evaluation will turn out to be invalid.The examples of randomness are:
- Initialization of the model
- Data sample
- Learning algorithm
For instance, a neural network includes weights initializing the random values. In contrast to different updates, the stochastic gradient descent will randomize the sample order of the data. To select the possible limit in a random forest, the selection of random subsets will be reassuring. It isn’t appropriate to consider randomization as a bug in a machine learning algorithm. This feature improves the model’s performance through traditional deterministic methods.
How is Minimizing Confounding Variables Important?
Comprising the confounding variable is the essence of ensuring the internal validity. Inability to reduce confounding variables from your research or model will not generate the actual relationship among two variables. As a result, you will encounter inconsistent results. Comparatively, the result you discover will include a cause and effect relationship, which isn’t the case in reality. Because the independent variable fails to producing the effect, you end up measuring the confounding variable.
Decrease the Effects of Confounding Value
Once you complete the research, utilize statistical methods to reduce the confounding effects in the model. The stratification method will increase the efficiency of the results, provided that the potential confounders are small in numbers. This method to reduce confounding variables comprises dividing the result into smaller groups. Hence, it separates confounding variable into groups. Next, observe the relationship between both variables, independent and dependent, in every group.
Suppose your research is on identifying smokers and non-smokers for the mortality rate also includes people with alcohol addiction. This will affect the result as alcohol use also affects morality. Using the stratification technique, create different small groups of smokers and non-smokers. In consequence, observe the relationship between alcohol use and mortality in each group.
Multivariate analysis will reduce the influence of confounding values in a model with a huge number of potential confounders. This analysis technique includes linear or logistic regression.
You will generate distorted results when you fail to modify the third variable affecting a relationship between two variables. Determination of the confounding variable is the essence for evaluation of the machine learning model. The model might include many unknown confounding factors, which changes the result. Your planning, designing, and executing the prediction model will be of no use as they will manipulate the independent variables. Hence, reducing the effects from the algorithm is necessary to yield error-free and specific results.