Artificial Intelligence (AI) is all around us, simplifying our lives in countless ways. Whether it’s answering everyday questions or suggesting your next favorite movie, AI works tirelessly to predict what might happen next. However, while traditional AI excels at making predictions, it often struggles to explain why things happen. That’s where Causal AI comes in.
Causal AI focuses on understanding cause-and-effect relationships, providing deeper insights beyond predictions. This emerging field is gaining traction, with the global Causal AI market valued at USD 26.0 million in 2023. According to Dimension Market Research, this market is projected to grow significantly, reaching an estimated USD 599.3 million by 2032.
In this blog post, we'll explore what Causal AI is all about, how it works, and what makes it stand out from other types of AI.
What is Causal AI?
Causal AI is an exciting form of AI that can think and make decisions a lot like humans do. It uses the concept of causality to go beyond the limits of traditional Machine Learning (ML), which is often seen as a "black box."
Causal AI is designed to keep the "human in the loop", allowing users to directly see how the AI has come to a conclusion so that the human can provide more contextual information, providing organizations with a reliable tool to address their most pressing challenges. As a result, Causal AI represents a pivotal evolution in AI, promising to redefine how businesses operate and innovate.
Jargon explanation:
- Black box: An Artificial Intelligence system whose inputs and operations aren't visible to the user.
- Human in the loop: The user(s) is able to see how the AI has come to a conclusion to add additional contextual information if needed. Casual AI is explainable, observable, understandable and auditable.
Moving beyond predictions
Machine learning usually focuses on making predictions. Causal AI can do that, too, but its real strength is helping you answer questions that regular machine learning models can't tackle. Simply put, while predictive analysis can identify an event and forecast a potential outcome, it cannot demonstrate that the outcome happened due to that event.
Correlation does not imply causation.
Causal AI stands out by identifying the true causes of events and their specific effects on outcomes. By using causal models and algorithms, businesses can better understand their operations. These models allow domain experts to use their knowledge in the process, making sure that relationships are accurately shown and useful in different situations.
In short, causal methods successfully merge domain expertise with data-driven techniques, allowing businesses to pose essential questions and extract more insightful conclusions about their processes. Examples of these questions might be:
- Why aren't customers finishing their purchases?
- What's leading to customer churn?
- Why is my employee attrition rate higher in 35-50 year olds?
Correlation AI vs. Causal AI: Weighing the differences
When it comes to data analysis, Correlation AI and Causal AI take very different paths. Correlation AI is all about spotting patterns and links between variables, which is great for making predictions but doesn't always explain why those patterns are there. Causal AI, however, digs into the "why" by establishing causal links. This difference is crucial because relying only on correlations can lead to wrong turns while understanding causality lays the groundwork for more strategic and impactful actions.
| Causal AI | Correlation AI |
|---|---|
| Clearly shows and explain what is happening at each step using specific data from the situation. | Clearly shows and explains what is happening at each step using specific data from the situation. |
| Uses facts to perform automatic analyses. | Relies on probability and needs humans to check if the results are correct. |
| Can adjust to new situations and discover things we didn't know we were missing. | Relies on finding patterns might not work well in new situations. |
| Explains how it reached a conclusion. | Can predict outcomes but struggles to explain why they happen. |
| Uses real-world data instead of training data, which helps avoid bias problems. | Based on correlation, uses statistics to make guesses about current events. |
How does Causal AI work?
Causal AI utilizes causal inference methods applied to observational data to represent the dependencies and causal connections among various events and variables. These causal models offer explainability by identifying the mechanisms that influence outcomes. Below is a step-by-step explanation of how Causal AI works:
1. Gather observational data
Causal AI systems start by collecting extensive observational data over time, which is the foundation for identifying causal relationships.
2. Uncover causal connections
Algorithms look at patterns in data to find possible cause-and-effect links between things. Techniques like causal discovery help identify these connections, which are then used to create a causal model.
3. Construct causal models
Causal models, such as Bayesian networks or structural causal models, show how different factors are connected and influence each other. They are built from patterns that reveal these cause-and-effect relationships.
4. Leverage domain knowledge
Domain experts improve causal models by setting or narrowing down known cause-and-effect relationships, combining data analysis with human knowledge and understanding.
5. Assess causal impacts
Causal models use techniques like counterfactual analysis to understand the effects of possible actions. They then study what happens when certain factors are changed.
6. Test
Use causal models to test interventions on a small scale or in simulations to predict their effectiveness before broader implementation.
7. Improve
As new data comes in, causal models are updated to become more accurate and helpful while staying clear.
Causal AI use case examples
Causal AI is making waves across a variety of sectors.
Finance
Causal AI in Fintech and finance can find the real reasons behind financial market changes, helping them make better trading and investment decisions. By understanding cause and effect, organizations can better predict the impact of policy changes, interest rate shifts, or market disruptions, leading to stronger and more profitable financial models.
Retail
In retail, using Causal AI can improve marketing strategies. For example, by giving coupons to customers who already like a product, retailers can use causal AI to see how these coupons affect sales. If the analysis shows that these customers buy more often, it makes sense to focus more on this coupon strategy. This not only increases sales but also builds customer loyalty, making it a valuable tool for retail success.
Healthcare
In healthcare, Causal AI helps us look at patient data to find the best treatment plans for people with chronic conditions like diabetes. By checking out how different medications and lifestyle changes directly affect patients, healthcare providers can create personalized treatment strategies that not only make patients feel better but also help save on healthcare costs.
Causal AI in practice
In our organization, we decided to explore Pi Predict, a predictive model from Panintelligence that leverages Causal AI, to analyze how well our marketing team's digital ads performed. The big question was: why do some digital campaigns generate leads while others fall short?
Initially, campaigns with pink backgrounds performed worse than those with dark blue or black backgrounds. Naturally, our human bias led us to conclude that color was the key factor. When viewing all the ads together, the patterns of color stood out, and we interpreted this as the cause—it seemed intuitive. However, this perception resulted from human processing, where we identified color as a meaningful pattern. We needed to challenge this assumption.
Using the causal decision tree, we uncovered a surprising truth: the sector targeted by the campaigns was the true driving factor. Campaigns aimed at Fintech and Retailtech sectors performed poorly regardless of color. As for the colors themselves—dark, light, pink, or blue - they had no significant impact on performance. The click-through rates were evenly distributed across all shades, once we accounted for other factors. What we perceived as "color" was just a surface pattern, not a causal element.
Another insight we derived was about gated versus non-gated content. Non-gated content generally performed better, which wasn’t a surprise. However, the causal model also revealed a more nuanced insight: Product Managers were likelier to engage with gated content. This helped us refine our targeting strategies.
What did we learn?
- Background colour did not have an effect. This was skewed by the human eye when laid out on the table.
- Campaigns to the Edtech and Healthtech stood out with high click responses.
- Retailtech and Fintech stood out with low click responses.
- Product Managers are discouraged from gated content.
The new causal discussions that this has generated are: Why are Edtech and Healthtech campaigns more successful?
- Do we have better referenceability in these sectors?
- Do people in these sectors have more time to engage?
- Are these sectors not being targeted by the competition?
Above is a good example of how Causal AI can be used at a low risk with a high reward. We understand what worked; now, let's improve.
