Predictions made by machine learning models are usually based on finding patterns from the data at hand. Hence, they run on correlation rather than causation. This leads to a gap in understanding how these models work. It creates a black box situation where the data being fed churns output without the knowledge of how it was done.
Moving from correlation to causation, to understanding the how and why of events seems crucial to paving the way forward in AI. Through this blog, we will gain a better understanding of why this shift seems imminent.
What Is Causal AI?
Causal AI helps to understand the underlying cause-and-effect relationships of an event that machine learning models usually fail to explain. It can also ask what-if questions and monitor interventions that can change the outcome. It helps to look inside the black box created by machine learning models to understand how and why an event occurred or might occur. Hence, it takes a step further from correlation to explain the cause-and-effect relationships.
With reasoning involved in the decision-making process, Causal AI can make more human-like decisions. Since human beings process things using cause-and-effect relationships, this also leads us a step closer to the goal of true AI.
Correlation vs Causation
When changing a variable in the dataset leads to some change in another variable, it can be deduced that the two variables are correlated. However, correlation does not necessarily imply causation. When a change in variable A results in a change in variable B while everything else remains constant, it is an instance of causation.
Correlations between variables in the data are directly visible, while causation requires a deeper understanding of the underlying cause-and-effect relationship between variables.
Why the Need for Causal AI?
When machine learning models use correlations to make predictions that are limited to a user’s next purchase or text generation, it works well. However, this might not be enough when such predictions are being used to make some crucial decisions regarding a patient’s health situation.
An important application of machine learning is finding unexpected patterns in the dataset and checking how true these patterns are. Again, these patterns represent correlations, and implying causations from them would be a mistake.
In most cases, the data being fed to the machine learning model cannot help the model learn directly what is required. This is because the thing to be measured does not always rely on a single variable.
Now, when we shift from correlation to causation, it helps us understand when a machine learning model might fail, how far it can be applied, and how long it can continue being predictive.
Applications of Causal AI
As the field of Causal AI evolves, researchers work towards its potential applications in healthcare and climate change.
1. Climate Change
Causal AI techniques applied to understand climate change were used to determine whether and to what extent the level of human contribution was a cause. To understand this, the European heatwave of 2003 was analyzed with and without the intervention of humans. It was determined that the chances of a heatwave are higher when air travel and electricity production become contributing factors.
2. Mortality Rates
To understand the cause of high mortality rates amongst mothers and their newborns in various countries, causal AI was deployed. Among the various other techniques used, the Surgo Foundation made use of casual AI to conduct a survey to understand the potential factors affecting institutional delivery.
The outcomes helped to know why women chose home deliveries over institutional deliveries and what could be done to change it. Some of the causes discovered were access to transportation over the proximity of the center and beliefs whether hospital deliveries were safer. This could change with an awareness of the financial incentives involved, having a delivery plan for institutional deliveries in advance, among other factors.
Causal AI draws a clear distinction from correlation and helps to understand some of the limitations of machine learning models. By helping us understand the why and how of the decisions being made, it widens the scope of AI. This also makes causality of the essence for the future of AI.