Hierarchical clustering as a solution to multicollinearity in causal inference

Multicollinearity represents one of the most persistent challenges in observation-based causal inference, especially when regression models are used to unravel the individual impact of explanatory variables that exhibit high correlations. In the business realm, where decision-making increasingly relies on data, this problem can completely distort the interpretation of causal effects. While classical solutions such as shrinkage estimators or principal component regression prove useful for prediction, they sacrifice the ability to recover original causal relationships. Faced with this limitation, an innovative approach emerges: hierarchical clustering applied to data aggregation to alleviate collinearity. This technique, originating in marketing mix model contexts, proposes grouping geographic units according to the correlation of their spending across different advertising channels, normalizing and removing common trends to then calculate pairwise distances and form clusters with moderate or strong correlations. Both descriptive and econometric evidence confirms that this approach effectively mitigates multicollinearity and facilitates the separate identification of each channel's effects. For organizations seeking to implement these methodologies, having custom applications is essential, as it enables building data pipelines, automating clustering processes, and scaling analysis to massive volumes of information. Furthermore, the integration of artificial intelligence solutions for businesses enhances the ability to detect hidden correlation patterns and optimize resource allocation. In this context, AWS and Azure cloud services infrastructure provides the necessary elasticity to run complex models without capacity restrictions, while business intelligence tools such as Power BI transform results into interactive dashboards that facilitate communicating insights to executive teams. Likewise, incorporating AI agents enables real-time monitoring of cluster stability and detection of changes in correlations, improving the robustness of causal inference. Of course, the security of sensitive data used in these analyses cannot be neglected; therefore, cybersecurity practices and penetration testing (pentesting) are essential to ensure the integrity and confidentiality of information. Ultimately, hierarchical clustering consolidates itself as a powerful and practical tool to overcome multicollinearity in causal inference, and its successful implementation depends on a technological architecture that combines custom software, artificial intelligence, and cloud platforms, just as Q2BSTUDIO can offer to companies seeking to advance their analytical maturity.