What are the Limitations of Machine Learning in Causal Analysis?

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SUMMARY

Machine learning (ML) excels at making predictions but struggles with causal analysis due to its reliance on data without human intuition. The discussion highlights that while ML can identify correlations, such as the link between low socioeconomic status (SES) and diabetes, it cannot deduce the underlying causal mechanisms without human insight. Participants argue that human analysts can leverage domain knowledge and deductive reasoning to uncover latent variables, which ML algorithms cannot do autonomously. The consensus is that current AI lacks the capacity for the nuanced understanding required for effective causal inference.

PREREQUISITES
  • Understanding of machine learning algorithms and their limitations
  • Familiarity with causal inference concepts
  • Knowledge of statistical significance and p-values
  • Basic grasp of human cognition and intuition in data analysis
NEXT STEPS
  • Research advanced causal inference techniques, such as Structural Equation Modeling (SEM)
  • Explore the limitations of machine learning in causal analysis through case studies
  • Study the role of domain knowledge in enhancing data interpretation
  • Investigate emerging AI methodologies that integrate human-like reasoning
USEFUL FOR

Data scientists, machine learning practitioners, and researchers interested in the intersection of AI and causal analysis will benefit from this discussion.

  • #61
I agree with the concept that machines have no limit, but comparing them to human intelligence seems arbitrary and anthropocentric. We'd also have to make clear that we're talking about very long periods of time. 100 years from now? Yeah, there's a lot of limits and I don't see a Bender Rodriguez strutting down the street in that amount of time, but they'll certainly be driving our cars and flying our planes. 1000 years? Mmm... maybe? 10,000, absolutely.

Of course, assuming humans destroy ourselves, which in theory, we don't have to.

It shouldn't really even be debatable of whether or not an AI could ever match a human. The human brain is a chemical machine, we have programmable maths that describes that chemistry. Given enough computing power, you could build a brain from atoms up inside a computer. It's unlikely we'd ever need to do anything like that and I don't see humanity having that much computing power any time soon, but there's nothing really preventing it in theory. The only real limit is energy and matter.

Natural selection gave us our thought process in about 600 million years, I'd think intelligent design could beat that by several orders of magnitude.I'm weary of AI in the long term. I don't think anyone alive today has to worry about it, but I see it as one of the potential "great filters" for life in the Fermi paradox. I see no reason to fear any individual AI, but the fact that they are immortal means that the number of them will grow very rapidly. I think they'll be very individualized, and be a result of their "upbringing." They'll be as diverse as humans and while I believe that most humans are good. .. Hitler existed.
 
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  • #62
A. Neumaier said:
Many classification problems solved by computers better than by humans are also not well-defined problems.
I don't doubt that at all, but I wouldn't lump all non-well-defined problems in the same category. There are several different degrees of being badly defined, some of which are still perfectly solvable - sometimes even with trivial ease - by some humans, despite all their vagueness.
 
  • #63
Auto-Didact said:
I don't doubt that at all, but I wouldn't lump all non-well-defined problems in the same category. There are several different degrees of being badly defined, some of which are still perfectly solvable - sometimes even with trivial ease - by some humans, despite all their vagueness.
Please give examples.
 
  • #64
A. Neumaier said:
Please give examples.
I'm currently publishing two papers on this topic; will link when they are done.
 

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