Spurious correlation refers to a situation in machine learning where a feature and the model prediction appear to be statistically correlated, but their relationship is coincidental or caused by some external factor rather than a genuine causal or meaningful connection. In other words, the relation between the feature and the model is not based on a true cause-and-effect relationship.
Causes of Spurious correlation#
Several factors can contribute to the occurrence of performance bias in machine learning models:
Confounding Variables: Spurious correlations may arise when there are confounding variables that influence both the predicted variable and the feature being considered. These variables can create an illusion of correlation between the feature and the prediction, even though they are not causally related to each other. It is important to carefully examine and account for confounding variables to avoid mistaking their effects for genuine correlations.
Data Noise: Spurious correlations can occur due to data noise or anomalies that are unrelated to the underlying problem. This noise may result from errors in data collection, measurement biases, data preprocessing issues, or other data-specific factors. If the model learns to exploit these anomalies, it can mistakenly identify them as meaningful correlations.
Random Chance: In some cases, spurious correlations can occur purely by chance. When working with large datasets or a large number of features, the likelihood of finding coincidental correlations increases. These correlations are not meaningful but are simply random occurrences that can mislead model predictions. This can happen with time-series data, where spurious correlations can emerge due to the presence of similar trends or seasonality.
Feature Overfitting: If the model has a large number of features relative to the available data, it may overfit the training examples. Overfitting occurs when the model learns specific patterns and noise present in the training data, including spurious correlations. As a result, the model fails to generalize well to new, unseen data, leading to unreliable predictions.
Addressing Spurious correlation#
Collecting domain knowledge and gathering business feedback are crucial steps in mitigating spurious correlations in machine learning. Here’s how these actions can help:
Gather Domain Knowledge: By engaging with domain experts and stakeholders, you can gain insights into the underlying business problem and understand the causal relationships between variables in the domain. This knowledge helps identify potential confounding factors and variables that may introduce spurious correlations. It also enables you to understand the context, business rules, and constraints that impact the interpretation of the data and model predictions.
Involve stakeholders: By sharing the model’s progress, findings, and challenges, you can gather feedback and insights from those with domain expertise. Through discussions, you can assess the plausibility of potential causal relationships identified by the model and gather feedback on correlations that align with the stakeholders’ knowledge. This collaboration ensures that the model’s predictions are evaluated and validated by experts in the relevant domain.
Explainability: Explainable models, such as decision trees or linear regression, allow domain experts to scrutinize the correlations identified by the model and evaluate their plausibility. Transparent and interpretable models provide a common ground for collaboration, understanding, and validating the relationships between the model’s predictions and the underlying causal factors in the business domain.
By incorporating domain knowledge and business feedback, you can ensure that the relationships identified by the model align with causal links in the real world. This collaborative approach helps validate the model’s findings, mitigate spurious correlations, and build trust in the machine learning solution.