Glossary term

Causal AI

In recent decades, predictive modeling has become an essential tool for many organizations, allowing them to make data-driven decisions and enhance their business operations. However, as competition gets fiercer and new challenges arise, enterprises have been left searching for new and improved techniques to be faster, smarter, and better than their competitors.

One innovative technique shaking up the data science space is causal AI. In this post, we’ll go over what causal AI is, why it’s important, and how organizations can generate value by leveraging insights from its results.

What is Causal AI?

While causal AI isn’t new, it has gained serious attention following the publication of the 2022 Gartner Hype Cycle for Artificial Intelligence, which cited causal AI as 5-10 years away from mainstream adoption with a high potential benefit.

Causal AI is an emerging form of machine learning that strives to go beyond traditional ML models. While traditional techniques identify the extent to which multiple events are related, causal AI identifies the root cause of events by understanding the effects of any variables that may have led to it, providing a much deeper explanation of their true relationship.

The Methodology Behind Causal AI

Central to the success of causal AI is causal inference; a methodology researchers use to determine both the independent effect of an event and to draw causal conclusions from data. On a basic level, this method focuses on three key criteria—that A happened before B, that B did not happen by chance alone, and that C, D, E…Z and any other variable could not have caused B. This methodology is the core logic behind causal AI and is how it’s able to identify insights beyond correlation.

If given a dataset of temperature, ice cream sales, and average sunburn, a traditional ML model would fail to see the difference in relationship between ice cream vs. temperature and ice cream vs. sunburn. Without human intuition, the model’s insights could be interpreted as when sunburn increases, then so must the consumption of ice cream. We as humans can obviously tell this is wrong, but a traditional ML model does not. Causal AI, on the other hand, can identify through causal inference that ice cream and sunburn are not dependent on one another, and are instead determined by an alternate variable, in this case, temperature.

While that may seem like a trivial example, it highlights the key difference in analytical behavior between traditional ML models, which identify correlations and patterns, to causal AI, which explains the cause and effect of model outcomes.

Why is Causal AI Important?

While AI can help businesses in a myriad of ways, traditional ML methods do have a few restrictions, including limited insights, narrow explainability, and a high susceptibility to bias. However, many of the intrinsic qualities of causal AI address these concerns and make causal AI a strong addition to any business’s data science toolkit.

Collaborative and Explainable

By shifting focus from correlation to causation, the design philosophy of causal AI lends itself to cross-team collaboration. Currently, many data science initiatives struggle to get buy-in from executive decision makers who don’t understand how models came to the conclusions they did. By identifying cause and effect, causal AI provides a much more human-like analysis and can instill more confidence in project supervisors than conventional ML models.

Protected from Bias

Particularly when creating models that deal with sensitive information, such as risk assessment or fraud detection use cases, it’s extremely important that there’s no inherent bias baked into the models. Conventional AI require significant supervision from expert data scientists and domain experts to ensure no bias interferes with the results. Causal AI dramatically reduces the need for manual supervision since by deriving the cause of an event, you are ruling out its potentially coincidental correlation with other events and preventing bias by design.

Simulating Hypothetical Outcomes

Causal AI is capable of simulating events to compute hypothetical outcomes for evaluation—in other words, efficiently answering “What if?” scenarios. This advantage can save enterprises extensive time and resources that would otherwise be spent on physical tests or other manual, tedious experiments.

How Can Businesses Apply Causal AI?

There are many ways businesses can leverage causal AI to level up their data science strategy—including enhancing marketing tactics, preventing churn, and analyzing investments.

Customer Journey

The customer journey is an important part of generating interest and attracting new clients. Marketers can leverage causal AI across the customer journey to not only understand what steps are working or not, but why they are succeeding or failing. This difference provides marketing teams the insights needed to efficiently target poorly performing areas, as well as improve areas that are already doing well.

Churn Prevention

When it comes to churn prevention, conventional ML models are already being successfully utilized by enterprises—however, causal AI has the potential to take it a step further. While ML models can identify and categorize customers by their likelihood of churning, causal models can also identify which customers will respond well to follow-up messaging and which won’t. These insights can improve customer retention as resources can be allocated toward more receptive customers.

Investment Risk Analysis

Amid economic uncertainty, causal AI is also being used more prominently in asset management and investment analysis to mitigate risk and maintain competitiveness. Casual AI can empower investors to explore vast arrays of data, identify unseen trends, and use insights into the cause of those trends to seize new investment opportunities.

To Wrap Up

As businesses become increasingly dependent on data-driven decision making, it’s important that the limitations of conventional predictive analytics do not interfere with business performance. Causal AI addresses many of the weaknesses of conventional AI, and when used in tandem, both can ensure that businesses continually receive fast and reliable insights.

To put your business in the best position, take a look at implementing causal AI into your own processes and see how it can help your enterprise gain a competitive advantage.

If you’d like to learn more about recent advancements in the data analytics industry, check out this article on five cutting-edge data science techniques to drive innovation.

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