Improving Artificial Intelligence Models Performance through Causal Inference

How next-generation AI frameworks transformed model outcomes, rebuilt trust in AI, and drove scalable business impact in one of the biggest automotive companies in the world

Our Client’s Challenge

A major automotive enterprise’s Chief Data Office (CDO) team was growing frustrated with the quality of Machine Learning (ML) models being deployed across the business. Key issues included:

Many model variables showed minimal causal relevance to real business problems.
Models frequently failed to achieve the desired outcomes, leading to missed opportunities and subpar returns.
The need for a more generalizable AI approach—one that could extend across multiple business units and use cases.

Faced with these shortcomings, the client sought a solution to reconnect model insights with actual business impact, focusing on trustworthy, outcome-driven approaches.

Key Objectives

Reestablish Trust in AI Models: Ensure model predictions align more closely with genuine causal drivers of business performance.
Improve Business Outcomes: Address inconsistencies in current modeling approaches and ensure models directly support key business goals.
Create a Scalable Framework: Develop a methodology that could be repeated for different problems across the organization, enabling consistent success at scale.

The AI COLLABORATOR Solution

Within just three weeks, AI Collaborator assembled a specialized team of Ph.D.-level causal inference experts to work alongside the client. This collaboration laid the foundation for multiple high-value initiatives.

Proof of Concept (POC) Causal Framework

Evaluated various techniques for identifying meaningful causal relationships in the client’s data.
Focused on improving the accuracy and impact of decisions linked to high-impact business problems.

Synthetic Data and Benchmarking

Created synthetic data using generative models, enabling safe and efficient testing of multiple causal hypotheses without requiring sensitive data.
Explored benchmark datasets to validate performance and pinpoint the most effective approaches for the client’s unique needs.

Practical Application

Guided the client in applying causal inference techniques to real-world business scenarios, further boosting internal confidence in AI solutions.
Provided clear best practices for integrating these methods into existing workflows and technology stacks.

Through these efforts, the team helped the client realize how causal AI models often outperform traditional ML approaches, with one internal study indicating performance gains of up to 42%.

Client Outcomes

Stronger Model Performance: Causal AI models demonstrated up to a 42% improvement in performance over state-of-the-art ML approaches, delivering more reliable insights and boosting overall model trust.
Clear Causal Insights: By isolating the true drivers of business outcomes, the client’s teams gained a deeper understanding of how to allocate resources and refine strategies effectively.
Repeatable Success: The newly created framework and best practices now serve as a model for tackling additional high-priority use cases across the organization, enabling swift and scalable adoption.

Overall, the collaboration between AI Collaborator and the client paved the way for increased trust in AI-driven decision-making, fostering a more confident and outcome-focused approach to deploying advanced analytics at the enterprise scale.

Client Testimonial

“The partnership on the causal work has been a win as we build muscle in this area. There’s quite a bit we’ve accomplished from a capability development and demonstration perspective.”

— Head of Advanced Analytics CoE (AACE), Chief Scientist for AI/ML and Operations Research