CausalDD helps answer questions such as:
Should I invest in this causal inference company? Can they deliver on their promise?
Should I buy from this causal inference company? Can they deliver on their promise?
Should I trust this causal inference expert? Can they deliver on their pomise?
Since Pearl's release of his "Book of Why" in 2018, interest in causality has experienced exponential growth. But not all causality is born the same.
Many promises have been made that cannot be delivered on. We can help you find the signal in the noise.
The company hires a professor to gain your trust and suggest that they are at the cutting edge of causal inference. What you might actually get is:
Not much involvement of the professor at all.
The professor does not even research probabilistic causal inference.
Rooted in academia, the professor naturally has no relevant experience of practical limitations of causal inference (at no fault of their own).
The company promises to "identify the causal drivers" of your business or "optimize the economy". What you might actually get is:
Gross misunderstanding of the limitations of causal inference ('no cause in, no cause out').
Destructive or at best useless recommendations for which causes drive your business.
Delayed adoption of causal inference where it actually could make a change.
The company references seemingly independent reports such as Gartner's "Use Causal AI to Go Beyond Correlation-Based Prediction" Here, it's important to differentiate what's new and what's old:
Most relevant causal business problems have had answers before "Causal AI" became trendy.
A\B Testing is just fine in most business contexts.
Which causal assumptions are trustworthy has been settled in the literature decades ago.
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