This could be why your ML model is Biased

Leaving out a relevant variable can cause the model to have a biased or misleading result!

Omitted variable bias, also known as confounding or missing variable bias, occurs when a relevant variable is not included in a statistical model, leading to a distortion of the estimated relationship between the variables that are included.

Essentially, the omission of a key variable can result in a biased and misleading analysis.

Let's illustrate this concept with an example:

Imagine you are studying the relationship between students' hours of study and their exam performance, and you create a simple linear regression.

The model with only the variable "hours of study" to predict exam scores.

However, you omit an important variable, such as prior academic performance.

If students who performed well in previous exams tend to study more, and their prior performance also influences their current exam scores, omitting the variable "prior academic performance" would introduce omitted variable bias.

In this case, the model might mistakenly attribute the positive effect of prior academic performance to the hours of study, leading to an overestimation of the impact of study hours on exam scores.

The true relationship is confounded by the omitted variable, and the model's results are biased.

In the context of data science and regression analysis, it's crucial to identify and include all relevant variables that may affect the relationship you are studying.

Failure to do so can lead to inaccurate conclusions and predictions.

To address omitted variable bias, researchers need to carefully consider potential confounding factors and include them in their models to obtain more reliable and unbiased results.

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