One of the biggest developments in the tech space for the past couple of years has been the mainstream adoption of artificial intelligence and machine learning. Businesses of all sizes have realized the huge opportunity to offer differentiated customer services with AI power. From intelligent product recommendations to personalized and automated conversations via voice and chat assistants, the field of deployment for AI-driven services is very vast.
As the driving force behind AI development, data science too witnessed massive strides in terms of skill sets, learning initiatives, new exploratory approaches, and much more. Data scientists are responsible for creating the models that power complex AI computations which in turn decides the accuracy of outcomes generated.
The need to answer “Why”!
However, if we look closely into the existing use cases of AI being adopted by businesses worldwide, a good majority of them can be traced back to a model that is built on the principles of statistical correlation. Most AI systems use predictive modeling to infer outcomes from a set of data. But as we move ahead with AI adoption at scale and in complex endeavors such as in healthcare, financial advisory, etc. there is a looming problem.
Stakeholders will expect AI systems to deliver more valuable insights than just outcomes.
One of the major requirements will be equipping them to tell why an outcome was derived from a particular input data stream and what happens or what outcome will be provided when the inputs are varied deliberately. For example, a hospital can use AI to study patient records and predict which patient will need more care and adjust their treatments accordingly. However, moving forward they will want to know why a specific patient is more prone to illness than the rest. This will help them in ideating large-scale preventive measures or working with the pharma community to develop drugs and vaccines to arrest the progress of the illness in potential patients.
This is where the importance of causality becomes a prime topic of interest. Data analysis should integrate causality as a key component of the models they build if it is to make a real impact soon. From an AI perspective, the right term for this is Causal Inference.
What Is Causal Inference?
For beginners, it refers to the study of cause and effect. For example, if there are two related events A and B, the causal inference is the study that seeks to find out why A caused B or B caused A, and also if some changes are made to A or B, how does it impact the other?
When translated into a business scenario, the above reference examples can be replaced by some of the most pressing questions most business leaders have about their offerings like “What changes to my customer experience can help improve sales? Or “Why are our lead generation efforts not yielding conversions?”
Implementing Causal Inference in Machine Learning
Traditional ML models work by implementing statistical correlation-based learning. In simple terms, they identify behavioral patterns in data streams and then conclude similar scenarios involving the data. However, when components within the data input streams have deeper and unclear relationships, ML models will not be able to accurately pinpoint outcomes.
Data scientists can enforce causal inference-based learning techniques to build more mature ML models. They use a diverse range of proven techniques and algorithms like Counterfactual Regression, Instrumental Variable usage, Bayesian optimization, Interrupted Time Series Design, Synthetic Control, etc. These can help ML models discover how different sub-elements within datasets control the behavior of connected entities and how variations can alter the relationship.
Why Is Causal Inference A Goldmine for AI Initiatives?
As we have seen above, the implementation of causal inference in ML models can help them decipher complex and interlinked relationships between data elements. From an application perspective, this will help in building AI models that can truly revolutionize areas of application. For example, a highly mature AI model that is built with integrated causal inference can accurately predict the potential growth opportunities of a stock in a trading environment. The model can work on how different internal and external stimuli on the specific business can impact its stock prices and help advisors prepare their clients for targeted investments.
Another example would be accelerated drug validation in healthcare. AI models with causal inference can help in deciding the right chemical constituents to be used for making drugs for complicated illnesses or medical conditions. Similar achievements can be accomplished in areas like public policy which can have a profound impact on the way governance is served or in neutralizing threats in cyberspace or preventing fraud, etc.
Causal Inference - An Essential Item in Your Data Analysis Toolkit
With AI expanding its usage horizon across industries and sectors, the need for data scientists to build reliable, and accurate computational models is of paramount criticality. Causal inference will play a vital role in optimizing the decision-making capabilities of AI systems and businesses can ultimately unlock new opportunities to compete in challenging markets using such capabilities.
However, taking the leap into implementing causal inference, or any AI capability, requires a more strategic foundation for the business’s digital ecosystem. There are plenty of challenges, from data frameworks to the right AI model usage. This is where a trusted technology partner like Wissen can be a great asset. Get in touch with us to know more.
