**Industry:** Automotive

**Technology:** Causal Inference

**Use Case:** Causal AI to Improve Machine Learning Models Performance

# 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**

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