Causality: The Missing Ingredient
Today’s generative AI capabilities are impressive. But just wait until one of the most important MISSING INGREDIENTS gets added into the mix. This ingredient is what will make AI a truly indispensable partner in business.
The missing ingredient is CAUSALITY.
Causality will enable businesses to do more than just create predictions, generate content, identify patterns, and isolate anomalies. They’ll also be able to play out countless scenarios to understand the consequences of various actions, explain causal drivers of their business and problem solve. They’ll know WHAT to do, HOW to do it and WHY certain actions are better than others — to more prescriptively shape future outcomes.
Causal AI is likely to become a “high” impact AI technology as indicated in the 2024 Gartner AI Hype Curve. And as a Databricks survey found, 6 in 10 plan to adopt it, making it the top future-state technology. Overall, its marketplace is projected 41% CAGR through 2030, nearly 2x traditional AI.
So, why is this such a big deal?
Well, let’s frame the role of causal reasoning by using an analogy to different functions of the human brain, which AI strives to mimic:
This analogy is right on!
Simply put, humans are causal by nature, so AI also needs to become causal by nature. And, in turn, create a interactive experience between human and machine to collaboratively problem-solve.
As such, this article on Causal AI will explore:
The basics
The ingredients
The use cases
Finally, I’ll outline some calls to action for those interested in getting more involved in this highly impactful technology.
Read on and let me know what you think!
The Basics:
The methods upon which AI reasons is its inferencing design — that is, how it progresses from a premise to logical consequences to judgements that are considered true based on other judgements known to be true. In essence, it must determine WHAT will happen, HOW it will happen and WHY it knows certain actions are better than others. This level of intelligence can only occur by fusing AI’s correlative powers with causality.
Let’s dive deeper, while making the analogy to the functions of the brain:
The WHAT — Today’s AI, including the LLMs / GenAI, derive their powers from correlating variables across datasets, telling us how much one changes when others change. They can deduct specific observations from generalized information or induce general observations from specific information. Auto-regressive designs also can describe random processes by identifying dependencies among variables and past values. LLMs are similar to the limbic brain which drives instinctive actions based on memories, making them good at automating tasks and creating content.
The HOW — Beyond predicting or generating an outcome (the “WHAT”), businesses also want to understand and explain how the outcome was produced. This requires advanced transformations that integrate correlative patterns, influential factors, neural paths and causal relationships (cause & effect) to de-code the “HOW”. Have you ever tried to explain something without invoking cause & effect? Think of this as playing the role of the cerebral cortex that encodes explicit memories into skills and tacit know-how. This is key to recommending prescriptive action paths that are trusted, transparent and explainable.
The WHY — For AI to truly reason and problem solve, it must understand precise “cause and effect” relationships. That is, understand the dynamics of “WHY” things happen, what can be done to change things, and the consequences of interventions. This requires a shift from today’s forward-progressive inferencing to bi-directional inferencing based on dynamic past conditions as a mechanism for exploring various “what-if” propositions. This mimics the neocortex which drives higher order reasoning such as decision-making, planning and perception. For AI that to collaborate with humans to problem solve, these powers are a must.
While there may be different degrees of implementation, causal AI is realized when it can deliver the capabilities above in an integrated manner — knowing the WHAT, the WHAT, and the WHY of business problems.
The Ingredients:
No matter how sophisticated a predictive model is, it still only establishes a correlation between a behavior or event with an outcome. But that is very different than saying that the outcome happened because of the behavior or event. There can be correlation but not causation, and visa versa. To make good decisions you need to understand root causes, as equating correlation with causation is an incubator for hallucinations, bias and false-positives.
Casual AI can identify precise cause & effect relationships. This is key because being able to predict something is not as valuable as actually knowing why and what can be done differently to improve outcomes.
Furthermore, the pathways through today’s neural networks are black boxes. They cannot tell you how variables interacted, their values or how they influenced an outcome. So why should you trust it? How do you explain it?
Causal AI understands the causal pathways to an outcome. It can infer the relationship among variables in a dataset, how variables influence each other, and the extend of those influences. This allows progressive discovery of complex causal relationships that are ranked (scored) in terms of influence.
With Causal AI, businesses will gain an array of new AI “ingredients” (tools):
Intervention: why certain actions are better than others (“what-if”)
Consequence: impact on metrics (KPIs) of alternate actions paths
Counterfactuals: evaluate alternatives to the current factual state
Confounders: identify irrelevant, misleading or unknown influences
Prescriptions: interrelated actions (pathways) to a desired outcome
Explanations: how and why a prediction or outcome was generated
Interrogations: infuse human knowledge, policy, and constraints
These new “ingredients” will enable businesses to play out countless scenarios while confidently understanding how actions impact outcomes.
Furthermore, they break open the “black box” of today’s AI to gain greater trust, explainability and transparency in how AI is helping to make business critical decisions, while simplifying regulatory compliance reporting.
In the end, seven key features of causal AI will finally enable humans and machine to collaboratively reason to solve complex challenges by from descriptive → to predictive → to prescriptive decision-making.
The Use Cases:
The use cases are truly limitless, as “cause & effect” is the foundation of decision intelligence, automation and strategic planning.
To illustrate, here are some real-world examples of causal questions:
Marketing: what is the best mix of tactics, spend, and offers?
Customer Churn: why are we losing clients in certain regional markets?
Pricing: how do we best configure an elastic pricing model?
Supply chain: what are root causes of inefficiencies and how do we fix?
Manufacturing: is inventory management a source of product failures?
Legal Strategy: what is the most legally sound mix of cited case law?
Financial: how will various fed rate cut strategies impact the S&P 500?
HR: how does various retail store staffing model impact revenue?
Regulatory: explain why we made more risky investments?
An array of companies are already talking these use cases using causal, including Netflix, John Deere, Georgia-Pacific, McKinsey & Company, Qualcomm, TIAA, BMW Group, GE Healthcare, CISCO, IBM, McCann, Aviva Investors, ASIN, Scotia Bank, to just ramble off a big handful.
For example, a real-life marketing challenge in retail:
Calls-to-Action:
Causal AI promises to deliver the AI reasoning and know-how key to solving more complex challenges. And since it complements existing AI investments, it’s cost-effective to explore how it can impact your future-state business.
This is because emerging causal AI models build off existing LLMs and can be easily deployed to users in flexible ways, such as “decision apps” in Microsoft Co-pilot. The possibilities are as flexible as they are endless.
Therefore, my view that Causal AI is inevitable and is the next frontier on the journey to. And the time is NOW to:
Explore the world of causal AI
Create a future-state strategy
Gain a hands-on experience
Engage pioneers to understand real-world ROI
Visit my causal AI website for more information on causal AI and feel free to contact me here or LinkedIn if I can help you get started.
