How to Use AI and Causality for Better Business Decisions
In today’s fast-paced and interconnected business landscape, making informed decisions is more critical than ever. Canadian businesses – whether startups, large enterprises, or government agencies – are dealing with increasing customer expectations, growing competition, and shrinking margins. The integration of Artificial Intelligence (AI) alongside causal inference methodologies offers a transformative solution, enabling organizations to refine strategies, test innovations, and make data-driven decisions.
This article delves into how AI and causality intersect, exploring their practical applications and how they can empower Canadian industries to stay ahead.
The Importance of Innovation and Experimentation in Modern Business
Businesses today face the dual challenge of innovating quickly while navigating uncertainty around whether new ideas will work. As Alex – an expert in AI and causal inference – points out, this tension is necessary. Borrowing from Jeff Bezos’ philosophy, "failure and innovation go hand in hand", and experimenting is essential for finding breakthroughs.
However, decision-making in modern businesses is far more complex than it once was. With many variables and customer touchpoints, relying solely on intuition or opinions can create blind spots. Organizations need a systematic, evidence-based approach to confidently evaluate new ideas.
The Role of AI in Business Decisions
AI enables businesses to scale decision-making processes in several ways:
- Volume and Speed: AI can process vast amounts of data and generate actionable insights faster than humans.
- Personalization: Companies can tailor customer experiences based on detailed behavioural insights.
- Testing at Scale: With AI, businesses can experiment with multiple options simultaneously, refining strategies for optimal performance.
However, AI’s true power is amplified when combined with causal inference – a scientific approach that helps distinguish causation from correlation. This allows businesses to understand not just "what is happening", but "why it is happening" and "what would happen if we made a change."
Causal Inference and A/B Testing: The Cornerstones of Innovation
What is Causal Inference?
Causal inference is the study of cause-and-effect relationships. Unlike correlation, which identifies patterns, causation seeks to pinpoint whether a specific action leads to a specific outcome. For example, is a new marketing campaign truly boosting sales, or is there another factor at play?
One of the most common tools for causal inference is A/B testing. A/B testing involves splitting users into two groups: a control group that experiences the current system and a treatment group that interacts with the new system. By comparing outcomes between these groups, businesses can determine whether the change delivers tangible benefits.
Why A/B Testing Matters
A/B testing has become the gold standard for testing ideas in industries such as e-commerce, technology, logistics, and healthcare. For instance:
- Political Campaigns: During Barack Obama’s presidential campaign, A/B testing different ad combinations resulted in a 40% increase in donations.
- Remote Work: A travel agency ran an A/B test to evaluate the impact of remote work on productivity, revealing a 13% improvement in performance.
Through experimentation, businesses can validate ideas without overcommitting resources. As Alex emphasizes, "Big wins from successful tests can easily compensate for the cost of multiple failed experiments."
When A/B Testing Isn’t an Option
While A/B testing is a powerful tool, it’s not always feasible. Businesses may face challenges such as:
- Customer Choice: If customers choose whether to accept an offer, this introduces selection bias, making it difficult to fairly compare outcomes between groups.
- Interconnected Systems: In cases where customers or systems interact heavily with each other – such as on platforms like Uber or Facebook – the behaviour of one user can influence others, complicating the analysis.
Overcoming Selection Bias with AI
Selection bias arises when the groups being compared are fundamentally different. For example, if a company offers a credit card, customers who accept the offer may already have a higher propensity to spend than those who decline. Without accounting for this, the impact of the credit card on spending may be undervalued.
AI and machine learning can address this challenge by:
- Predicting Propensity: AI models can estimate the likelihood of a customer accepting an offer based on observable factors such as demographics or past behaviour.
- Balancing Comparisons: By combining propensity estimates with predicted outcomes, AI can create an artificial balance between groups, allowing for fairer comparisons.
This approach ensures that businesses can make data-driven decisions, even in scenarios where traditional A/B testing isn’t viable.
Navigating Interference in Interconnected Systems
Interference occurs when the actions of one individual affect the experience of others. For instance:
- In ride-sharing platforms like Uber, offering an incentive to one rider can impact availability and wait times for others.
- On social media platforms, changing the user interface for one group of users may influence the content engagement of others.
To address interference, businesses can:
- Design Experiments Carefully: Structure tests to minimize overlap and ensure that observed effects are attributed to the change being tested.
- Use Advanced Modelling: Apply sophisticated causal inference models to account for indirect effects in interconnected systems.
Practical Applications for Canadian Businesses
Canadian organizations across various industries can benefit from integrating AI and causal inference into their decision-making processes.
- Energy: Test different energy efficiency campaigns to identify the most effective way to reduce consumption.
- Healthcare: Evaluate the impact of telemedicine platforms on patient outcomes.
- Logistics: Optimize delivery routes using AI-driven analysis of customer preferences and traffic patterns.
- Public Sector: Test policy changes (e.g., tax incentives or public transit discounts) to assess their effectiveness before full-scale implementation.
- Retail and E-Commerce: Experiment with website layouts or promotional offers to drive conversions and improve customer experience.
Even small businesses can adopt A/B testing or simpler causal methods to optimize operations. For example, a small restaurant could experiment with menu pricing or reservation policies to improve profitability.
Key Takeaways
- AI and Causality Together Drive Smarter Decisions: AI accelerates decision-making, while causal inference ensures those decisions are rooted in evidence.
- A/B Testing is a Game Changer: It allows businesses to test ideas systematically and reduce risks associated with innovation.
- Selection Bias Can Be Overcome: AI can help create balanced comparisons, even when customers self-select into groups.
- Design Matters in Interconnected Systems: Careful experimental design and advanced modelling are critical for businesses dealing with interference.
- Applications Span Industries: From energy and healthcare to logistics and retail, causal methods can provide actionable insights.
- Innovation Requires Experimentation: Don’t fear failure; successful experiments often outweigh the costs of unsuccessful ones.
Conclusion
Canadian businesses aiming to modernize and stay competitive must look beyond intuition and embrace data-driven decision-making. By integrating AI with causal inference methodologies, organizations can navigate complexity, reduce risk, and unlock new opportunities for growth.
Whether you’re a startup testing a marketing campaign or a government agency evaluating public policy, the combination of experimentation and advanced analytics can provide the clarity needed to make transformative decisions.
As Alex aptly concludes, businesses today operate in complex, interconnected systems, but the tools and methodologies to succeed – A/B testing, causal inference, and AI – are more accessible than ever. The key is to leverage these tools effectively, prioritizing both innovation and evidence-based strategies.
Source: "Business Decisions with AI: Causality, Incentives & Data" – Duke University – The Fuqua School of Business, YouTube, Aug 27, 2025 – https://www.youtube.com/watch?v=-uSU4f0dqTc
Use: Embedded for reference. Brief quotes used for commentary/review.
