One place might be to look at Granger Causality. I believe that causal ML can look for patterns in data that appear to conform to granger causality structures (ie there’s a leading and lagging indicator, if one always tracks the other then we can start to consider causality).

Normally causality is established in an experiment or natural experiment where we can isolate factors but since we have so much transactional data we can start to see patterns that resemble these structures without delineating the natural experiment ahead of time.

But causality is often very hard outside of very careful structures and it’s still a very active area.

One related place to look is also at network models like SAOMs which use panel data to explore issues with selection versus influence.

Sure but isn’t it the case that the H100 is what can sustain such a high throughput system whereas A100s are generally independent?