Causal Popularity: A New Model for Artificial General Intelligence (AGI)

The research at Estimand is currently centered around Causal Popularity, a combination of cause and effect detection with measurement of popularity. While still in its infancy, this is a critical first step toward AGI.

Why it matters: The current means to build AGI using statistical methods is contrary to how humans think and is making things more complicated than they need to be.

It started with causal detection

While building a causal detection model, many Data Scientists declared it was impossible without a human expert. We disagreed and began experimenting. Using a form of backtesting to prove that X and Y are connected with the same equation across time, we declared success. Training on financial data, we realized multiple unexpected connections, such as banana prices and interest rates.

Root cause issue

The problem with our first approach is that it does not always discover the root cause. Instead, it can falsely detect an indicator as the actual cause. To find the root cause, we must use graph theory and the notion of popularity. This is similar to the PageRank algorithm of Google Fame [Easley & Kleinberg 2010].