Exploring causality, confounders, and the future of intelligent systems through interactive demonstrations
Most AI systems excel at finding patterns and correlations, but struggle with understanding causality - the fundamental "why" behind relationships. This page explores how we can build AI systems that understand cause and effect, moving beyond correlation to true causal reasoning.
Explore different aspects of causality through these interactive demonstrations:
Explore the relationship between education level and income - a classic example of correlation that often implies causation.
Build and explore the classic "wet grass" causal graph - demonstrating how multiple causes can lead to the same effect.
Learn to identify confounding variables using the coffee-heart disease example, where smoking is the hidden confounder.
Explore "what if" scenarios through medical intervention - comparing outcomes between different treatment options.
Discover why ice cream sales and shark attacks are correlated - a perfect example of how correlation doesn't imply causation.
Compare different approaches to causal discovery and their effectiveness:
As we advance towards more sophisticated AI systems, the ability to understand causality becomes crucial. Future LLMs and AI systems that can reason about cause and effect will be better equipped to:
The integration of causal reasoning with large language models represents a promising frontier in AI development, potentially leading to systems that don't just predict what will happen, but truly understand why it happens.