Current machine learning platforms largely fail to provide time-series predictions because “correlations that have held in the past may simply not continue to hold in the future,” the London-based company causalLens notes. That’s a particular problem in areas like finance and business where time-series data types are ubiquitous.
Those correlations tend to be single data points, unsuited to capturing context or complex relationships. In one example, an algorithm can be given access to a data set about dairy commodity prices to predict the price of cheese. The algorithm may conclude that butter prices as a guide to predicting the cost of limburger.
Eluding the algorithm is a fundamental assumption about the cost of dairy products: the hidden common cause of price spikes for cheese and butter is the cost of milk. Therefore, a sudden change in the price of butter—consumers’ preference for olive oil, for instance—is unrelated to milk prices. Hence, the faulty correlation between butter and cheese can’t be used to predict the latter’s price.
The company touts its “causal AI” framework as looking beyond correlations to learn obvious relationships and then “propose plausible hypotheses about more obscure chains of causality,” it noted in a recent research bulletin. The approach allows data scientists to add domain knowledge and real-world context to improve predictive analytics. Causal AI proponents also argue their approach makes better use of data to come up with more accurate predictions through the framework’s ability to simulate different scenarios.
The original article can be found here.
It is useful to note that AI and causal inference has been contributed by professor Judea Pearl, who was awarded Turing Award in 2011. In 2020, Professor Pearl is also awarded as World Leader in AI World Society (AIWS.net) by Michael Dukakis Institute for Leadership and Innovation (MDI) and Boston Global Forum (BGF).