Topic 36: What is Causal AI?

Traditional AI, especially machine learning, is mostly focused on finding patterns in data. It learns correlations between inputs and outputs, that’s why it’s powerful for predictions, but not always explanations or decision-making. It doesn’t know why things happen, just that they tend to happen together.

To properly explore why something happens using AI, we need other systems that will focus on cause-and-effect relationships. And Causal AI can figure this out. It answers more difficult practical questions like: “What will happen if we change the treatment?” and “Would the patient still have recovered if they hadn’t taken the medication?” The main idea and benefit is to understand how different things influence each other and analyze what this can cause potentially. Causal AI helps with decision-making, planning, and "what-if" questions – areas where regular AI falls short. In fields that require thorough investigation and creativity, Causal AI, with its ability to construct and analyze "what if" scenarios, would be an excellent assistant! But we don’t talk much about Casual AI. It is still largely academic or niche-industry focused, but it might be crucial for achieving human-like reasoning and AGI. So today, let's explore this fascinating topic from the basics, see how it’s applied in the real world, and consider what it could mean for the future of AI.

In today’s episode, we will cover:

• The main idea behind Causal AI

• Will Causal AI help us reach AGI?

• The basics of Causal AI

  • Causal inference

  • Directed Acyclic Graphs (DAGs) and Structural Causal Models (SCMs)

  • The do-operator

  • Do-calculus rules

  • Counterfactuals: The "What Ifs"

• Causal discovery process

• Real world influence

• Conclusion: Why is Causal AI important for the future of AI?

• Sources and further reading

The main idea behind Causal AI

Traditional AI/ML models operate in an associational mode – they infer patterns from observational data and can predict outcomes from inputs, but they cannot reliably say what would happen under an intervention not seen in the data. Causal AI goes beyond prediction to enable explanation and intervention. It seeks to identify causal relationships that indicate how changes in one factor will influence others. This enables models to not only predict but also to explain and act.

This also makes AI more human-like in reasoning. Think of the word “cause,” and you will see that the idea of Causal AI is to always answer questions, such as “What will cause improvements?”, “Why something happens?”, “What if we did something different?”

Understanding the "why" behind actions and outcomes and being able to work with "what if" scenarios are the main features of Causal AI compared to the models we are used to working with. We’d like to call it the foundation of critical thinking of models – this allows one not just to follow rules to receive rewards while learning patterns, but to truly "understand" and analyze why something works or doesn't work in a particular way.

The Turing Award winner Judea Pearl, often referred to as the father of Causal AI, laid the theoretical groundwork for how machines can reason about cause and effect using tools like do-calculus and causal graphs (we’ll cover these terms in next section).

In his book "The Book of Why," co-authored with Dana Mackenzie, he proposed the concept of the ladder of causation with three levels of causal reasoning:

• Level 1: Association/Seeing involves finding patterns in observational data.

• Level 2: Intervention/Doing refers to predicting future effects of deliberate actions (the do-operator).

• Level 3: Counterfactuals/Imagining involves reasoning about hypothetical scenarios (what would have happened if something was different).

Traditional machine learning mostly lives on the first level. Causal AI opens up the upper levels, helping answer why something happened and what might happen​. Causal AI builds upon the formal language of causal inference. But first – why do we decide to cover Causal AI at all?

Will Causal AI help us reach AGI?

As we wrote in our previous article about World Models, the integration of Causal AI could make these models far more powerful. And when it comes to AGI, Causal AI may not just be useful – it could be essential.

In his book The Path to AGI, John Thompson – we are honored to have John as one of our longest-standing subscribers – frames AGI as a trifecta: Foundational AI (traditional machine learning), Generative AI (GenAI) (today’s headline-grabber), and Causal AI – each playing a critical role in building true intelligence.

Thompson argues that the future lies in Composite AI – the convergence of all three domains – gradually evolving into AGI through integrated, pragmatic development. We find this theory fascinating (do read John’s book!) and want everyone to be more aware of Causal AI. For now, it remains mostly in the academic shadows, but as you’ll see from the article below, its importance may be greater than most assume. Let’s learn →

The basics of Causal AI