Causal AI: Bridging the Gap Between Correlation and Causation

Causal AI
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Knowing which steps to take now to optimize future outcomes is incredibly valuable but challenging, requiring AI technology with human-like reasoning. Current models rely heavily on correlations, which aren’t enough to understand the impact of potential actions on our goals. 

To manipulate a system effectively, we need to understand its workings by identifying causal relationships. This insight is at the heart of causal AI, the first prescriptive AI technology that guides us to make the best decisions.

For example, consider a healthcare application where doctors want to understand the impact of different treatment protocols on patient recovery rates. By isolating variables, causal AI can pinpoint which treatments are most effective, providing clear insights into patient care. 

Read on to learn more about causal AI and how it can impact various fields, from agriculture to healthcare and beyond.

What is Causal AI?

Causal AI is a part of artificial intelligence that studies cause-and-effect relationships in information. It differs from conventional large language models (LLMs) and generative AI (genAI), which usually detect correlations only. This technology employs causal inference to recognize reasons behind events in datasets, making them more interpretable and less biased. 

Causal inference works by isolating the independent effects of events using techniques:

  1. Causal discovery –  involves using algorithms to analyze data patterns to identify potential causal relationships and develop models that represent cause-and-effect dependencies between variables.
  2. Structural causal models – these models use domain expertise to enhance the understanding of causal mechanisms. These models also estimate the effects of interventions by analyzing hypothetical scenarios and counterfactuals.

Such an approach enables causal AI systems to give correct answers regarding the influence made by altering specific variables, thus outperforming traditional machine learning (ML) capabilities.

Generative AI has succeeded at simulating human-like conversations and producing creative texts, leading some individuals to suggest that it has passed the Turing test. However, these models rely on probabilistic predictions, often producing hallucinations or incorrect outputs. 

They lack true causal reasoning, crucial for critical thinking and decision-making. This gap is significant when considering the development of artificial general intelligence (AGI), as AGI needs a deep understanding of cause and effect.

Judea Pearl significantly advanced the principles of causal AI in his 2000 research, “Causality: Models, Reasoning and Inference,” published by Cambridge University Press in 2003.

Video source: YouTube/Elja’s World of Wonder

How Causal AI Works

Causal AI is a powerful tool that models how events impact the world over time, enabling predictions of future outcomes. Unlike traditional ML models that capture static correlations, causal AI can adapt to environmental changes, addressing nonergodicity, whereas conventional models fail when conditions change. 

Without understanding the causes of events, traditional AI predictions are often backward-looking and inaccurate, a limitation causal AI overcomes.

Fault tree analysis is a great example of causal AI in action. This method starts at the root of a problem and works downwards, using Boolean logic to reveal events leading to system failures and linking component faults with system malfunctions.

Here’s how causal AI typically works:

  1. Data Collection: Gather extensive observational data that captures relevant events, behaviors, and metrics.
  2. Causal Discovery: Use algorithms to sift through this data and identify potential causal relationships.
  3. Building Causal Models: Create models like Bayesian networks (graphical models that use nodes and directed edges to represent and compute the probabilistic relationships among a set of variables) or structural causal models to represent these relationships.
  4. Integration of Domain Expertise: Refine these models by incorporating insights from experts in the field.
  5. Estimation of Causal Effects: Use counterfactual analysis to assess what would have happened under different conditions.
  6. Testing Interventions: Run small-scale tests or simulations to predict the effectiveness of potential interventions.
  7. Iteration on Models: Continuously update the models with new data to improve accuracy and provide ongoing insights.

By combining domain expertise and causal inference methods, causal AI explains why events happen and predicts what could happen under different intervention scenarios. This approach enhances decision-making, making it more transparent and involving humans more deeply. 

Causal models are explainable and debuggable, allowing for greater trust and verification of results. Unlike traditional models that need to be trained from scratch, causal models can incorporate human expertise during their design.

Addressing the “Black Box” Problem with Causal AI

The ultimate goal in AI development is explainable AI (XAI). XAI aims to clarify how AI models make decisions, helping us understand the rationale behind AI outputs before trusting them for significant decisions in our careers, lives, or businesses.

Causal AI goes further by requiring models to explain their predictions. In simple terms, it might use a graph to show cause-and-effect relationships. More advanced forms provide explanations in plain text, making them more accessible to humans.

Typically, traditional AI models lack auditable trails in their decision-making processes. They have no self-reporting mechanisms and no way to scrutinize the increasingly complex algorithms behind their outputs. This “black box” nature makes it challenging to discern the factors influencing individual results, leading to difficulty fully trusting AI outputs.

However, AI systems can also uncover insights from massive datasets that humans might overlook. This task is like looking for a needle in a haystack: instead of going through each straw manually, you employ a powerful magnet – AI – that quickly identifies where and provides reasons for how it found it.

Prioritizing explainability in AI systems can increase the reliability and value of various applications, making their outputs more dependable.

Real-World Examples of Causal AI

Here are a few causal AI examples that are changing decision-making and creating impact:

Precision Agriculture

AI models use satellite imagery and sensor data in precision agriculture to forecast crop yields, identify plant diseases, and classify plant species.

Causal AI goes beyond these capabilities by analyzing how soil quality, weather patterns, and irrigation practices impact crop health. For example, it might determine that adjusting irrigation based on specific soil moisture levels can optimize crop growth, enabling farmers to make more precise interventions.

Streamlining Supply Chains

A supply chain system has many highly interrelated parts. Such complexity can cause various disruptions, either due to fluctuations in demand, problems with logistics, or even external events. 

Causal AI helps disentangle this complexity by pinpointing the root causes of disruptions, whether they stem from demand fluctuations, logistical issues, or external events. This understanding allows for proactive optimization of supply chain processes.

Enlightened Financial Decisions

Numerous factors influence financial markets during trading periods. Traditional predictive models typically show relationships between variables based on statistical associations but do not reveal the underlying mechanisms. 

Causal AI provides deeper insights by helping finance leaders understand how investment decisions are influenced by and interact with market sentiment and economic indicators. This more in-depth insight allows for more informed and strategic choices, considering correlations and the actual cause-and-effect relationships within the market.

Guiding Healthcare Interventions

Causal AI can significantly enhance treatment selection processes within healthcare systems by analyzing patient outcomes from different medications over time. It examines historical records of individuals with similar health conditions who received various treatments. 

Identifying which treatments led to better health outcomes enables more effective and personalized medical care. This approach helps tailor treatments to individual patient needs, improving overall healthcare service delivery and patient satisfaction.

The bottom line is that causal AI isn’t just about predicting outcomes – it’s about uncovering the underlying causes and prescribing actionable steps for improvement. 

Future Implications of Integrating Causal AI

Causal AI is a promising yet nascent field that is still undergoing development. Currently, its technology faces higher error rates, especially in areas with insufficient training data, but these are expected to decrease as it matures.

A significant challenge in decision-making is achieving rapid, intuitive thinking while ensuring actions are well-grounded. Integrating genAI with causal AI addresses this by combining the quick, intuitive analysis of genAI with the thorough, methodical insights of causal AI. This blend allows for effective handling of cause-and-effect challenges, offering both speed and accuracy.

For instance, evaluating the impact of a pricing change on sales volume and the supply chain requires extensive analysis. With the combined power of genAI and causal AI, one can input the query into an LLM, which uses advanced causal models to provide insightful analysis. This approach reveals the direct consequences of past actions and explores future scenarios, aiding strategic planning.

The collaboration between genAI and causal AI enriches decision-making by enabling natural language queries, responses, and detailed, understandable explanations. This integration is crucial for understanding cause-and-effect relationships, providing a reliable, scalable, and explainable framework for decision-making across various fields. As such, enhancing genAI with causal AI represents a significant step forward in harnessing AI’s full potential.

Causal AI: Key Takeaways

To manipulate a system effectively, we need to understand its workings by identifying causal relationships. Causal AI, the first prescriptive AI technology, guides us to make the best decisions by uncovering these relationships.

For example, in healthcare, causal AI can identify which treatments lead to better patient outcomes by analyzing historical data. Similarly, in finance, it helps leaders understand how investment decisions interact with market indicators, leading to more informed choices.

Causal AI does not merely predict outcomes; it suggests actionable steps after understanding the root problem. This technology establishes a dependable framework for decision-making in different areas, which boosts transparency and trust because everything is accountable.

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Neil Sahota
Neil Sahota (萨冠军) is an IBM Master Inventor, United Nations (UN) Artificial Intelligence (AI) Advisor, author of the best-seller Own the AI Revolution and sought-after speaker. With 20+ years of business experience, Neil works to inspire clients and business partners to foster innovation and develop next generation products/solutions powered by AI.