Update on Causal AI

MIT Tech Review weighs in here on role of causal analysis in AI.  They position this as a future direction, however there are companies today very actively applying these ideas. GNS Healthcare is the example given here.  Babylon Health is another organization making a big investment in this field.

Below are some highlights from the Tech Review article.

Points to note:

  • The causal AI technique is being used in combination with traditional medical tools (such as clinical trails) to create more effective overall system.
  • This combination of AI and traditional tools provides a more thoroughly validated result.
  • Conclusions based on causal reasoning are fundamentally more generalizable than those based on correlations alone.
  • The inherent “explainability” of this particular causal technique is what enables it to be sued effectively in combination with this more traditional tools.

Artificial intelligence won’t be very smart if computers don’t grasp cause and effect.

…. artificial intelligence has glaring weaknesses. Machine-learning systems can be duped or confounded by situations they haven’t seen before. A self-driving car gets flummoxed by a scenario that a human driver could handle easily. An AI system laboriously trained to carry out one task (identifying cats, say) has to be taught all over again to do something else (identifying dogs). In the process, it’s liable to lose some of the expertise it had in the original task. Computer scientists call this problem ‘catastrophic forgetting.’ 

growing consensus that progress in AI will stall if computers don’t get better at wrestling with causation

causal Bayesian networks—software that sifts through large amounts of data to detect which variables appear to have the most influence on other variables. For example, GNS Healthcare, a company in Cambridge, Massachusetts, uses these techniques to advise researchers about experiments that look promising. In one project, GNS worked with researchers who study multiple myeloma, a kind of blood cancer. The researchers wanted to know why some patients with the disease live longer than others after getting stem-cell transplants, a common form of treatment. The software churned through data with 30,000 variables and pointed to a few that seemed especially likely to be causal. Biostatisticians and experts in the disease zeroed in on one in particular: the level of a certain protein in patients’ bodies. Researchers could then run a targeted clinical trial to see whether patients with the protein did indeed benefit more from the treatment. ‘It’s way faster than poking here and there in the lab,’

… human scientists designing an experiment can consider only a handful of variables in their minds at once. A computer, on the other hand, can see the interplay of hundreds or thousands of variables. Encoded with “the basic principles” of Pearl’s causal calculus and able to calculate what might happen with new sets of variables, an automated scientist could suggest exactly which experiments the human researchers should spend their time on.

… fundamental knowledge about the world can be gleaned by analyzing the things that are similar or ‘invariant’ across data sets. Maybe a neural network could learn that movements of the legs physically cause both running and dancing. Maybe after seeing these examples and many others that show people only a few feet off the ground, a machine would eventually understand something about gravity and how it limits human movement. Over time, with enough meta-learning about variables that are consistent across data sets, a computer could gain causal knowledge that would be reusable in many domains

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