Skip to content

Causal inference: the future paradigm of artificial intelligence

Causal inference: the future paradigm of artificial intelligence

“What people call AI is no more than using correlation to find answers to questions we know to ask. Real AI has awareness of causality, leading to answering questions we haven’t dreamed of yet.”

Tom Golway, information technology executive

Causal inference is the intellectual discipline that accommodates researchers to use data to draw causal conclusions by determining the independent effect of an element in a system (“cause and effect”). In our daily lives, we use causal inference to navigate the world as we face situations and solve problems. Many disciplines have an inherent interest in the concept of causality: economists, epidemiologists, sociologists, researchers, statisticians, and data scientists. Frameworks for causal inference include the Rubin causal model (also known as the potential outcomes framework) and structural equation modeling.

In healthcare, most studies aim to answer causal rather than associative questions. The classic manner in which causality is determined in clinical medicine and healthcare is the randomized controlled trial (RCT). Ethical and practical limitations, however, sometimes affect the deployment of an RCT. With the excitement about machine and deep learning in healthcare, a relative weakness of machine and deep learning methodologies in healthcare is that these tools, as good as they can be, usually do not provide causal inferences as these methodologies provide instead, correlations (correlation does not imply causation). The phenomenon of two variables having a high degree of correlation is termed multicollinearity. The prediction of an alternate reality is called counterfactual inference. Similar to its alternate inference (causal inference), counterfactual inference also has its own frameworks such as  generative and Bayesian modeling. Counterfactuals are essential for causality as we often cannot test more than one element.

Machine and deep learning have yielded impressive dividends thus far, but cannot be the sole paradigm for solving problems. The future of AI, especially as we aim to reach artificial general intelligence (AGI), will need to learn similarly to how a child learns: understand casual relationships without overabundant data.

Dr. Anthony Chang’s book is available on Amazon now! 

Intelligence-Based Cardiology and Cardiac Surgery Intelligence-Based Cardiology and Cardiac Surgery: Artificial Intelligence and Human Cognition in Cardiovascular Medicine (Intelligence-Based Medicine: Subspecialty Series) Data Science, Artificial

Subscribe to our mailing list and receive a FREE e-book!

Written by Dr. Anthony Chang, the Medical Intelligence Compendium and Glossary provides a comprehensive oversight into the terms and concepts that are crucial to the growing field of Artificial Intelligence in healthcare. Subscribe for a free copy today!

E-Book Request
Show Buttons
Hide Buttons