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This isn't just a technology problem. Most humans are also really, really bad at distinguishing causation from correlation. The very notion that these are even

This isn't just a technology problem. Most humans are also really, really bad at distinguishing causation from correlation. The very notion that these are even different things is utterly alien to most people. An AI that was even slightly good at it would be far ahead of what we get when humans run things.

nileshtrivedi 9y ago
There is no free lunch. Any forced adjustments we make to reduce false positives, will increase the false negatives. It's up to us to decide what trade-off we want and the algorithms will simply adapt to that. If we believe certain attributes to be irrelevant to a situation, we need to build systems that are completely blind to these attributes. This is how double-blind trials, or scientific peer review works.
- nl 9y ago
  Any forced adjustments we make to reduce false positives, will increase the false negatives. That's just not true. You can have systematic errors caused by bad training data which can be fixed without an increase in false negatives (otherwise new ML systems would never improve over old ones!) In the physical world a good analogy is crash testing of cars. For decades (until 2011!!), crash test dummies were all based on average sized American males. That led to hugely increased risks for female passengers: the female dummy in the front passenger seat registered a 20 to 40 percent risk of being killed or seriously injured, according to the test data. The average for that class of vehicle is 15 percent. Fixing that problem didn't case any increase in accident risk for men. It's the same in machine learning. There is no free lunch This isn't what the no free lunch theorem[2] says. That says that all optimization methods are theoretically equivalent. [1] https://www.washingtonpost.com/local/trafficandcommuting/female-dummy-makes-her-mark-on-male-dominated-crash-tests/2012/03/07/gIQANBLjaS_story.html https://www.washingtonpost.com/local/trafficandcommuting/fem... [2] https://en.wikipedia.org/wiki/No_free_lunch_theorem https://en.wikipedia.org/wiki/No_free_lunch_theorem
gowld 9y ago
"Causation" is defined as temporal correlation: changes in A cause changes in B. There's no reason to thin computers would be "bad" at this. People are only bad at this because they have small, lazy brains Even the greatest physicists and mathematicians can't tell the difference between correlation and causation. When people say "causation is not correlation", what they truly mean is "spurious, context-sensitive correlation is not strong universal correlation"
- joshuamorton 9y ago
  No. Temporal correlation is "changes in A precede changes in B". This is not causation, you more or less correctly describe causation when you say "Changes in A cause changes in B", but a better way of putting it might be "all else equal, changes in A cause changes in B". The difficulty here is controlling for all possible C. As a simple example: If A causes a change in both B and C, but the change in B happens more quickly, "temporal correlation" would imply that B causes C, when that's not the case. This is especially obvious with cyclical phenomena. The tide going out does not cause the sun to rise, for example, even though I'm sure I could draw a temporal correlation between them. Nor does my car being in my garage cause me to go to work.

nileshtrivedi 9y ago

There is no free lunch. Any forced adjustments we make to reduce false positives, will increase the false negatives. It's up to us to decide what trade-off we want and the algorithms will simply adapt to that. If we believe certain attributes to be irrelevant to a situation, we need to build systems that are completely blind to these attributes. This is how double-blind trials, or scientific peer review works.

nl 9y ago
Any forced adjustments we make to reduce false positives, will increase the false negatives. That's just not true. You can have systematic errors caused by bad training data which can be fixed without an increase in false negatives (otherwise new ML systems would never improve over old ones!) In the physical world a good analogy is crash testing of cars. For decades (until 2011!!), crash test dummies were all based on average sized American males. That led to hugely increased risks for female passengers: the female dummy in the front passenger seat registered a 20 to 40 percent risk of being killed or seriously injured, according to the test data. The average for that class of vehicle is 15 percent. Fixing that problem didn't case any increase in accident risk for men. It's the same in machine learning. There is no free lunch This isn't what the no free lunch theorem[2] says. That says that all optimization methods are theoretically equivalent. [1] https://www.washingtonpost.com/local/trafficandcommuting/female-dummy-makes-her-mark-on-male-dominated-crash-tests/2012/03/07/gIQANBLjaS_story.html https://www.washingtonpost.com/local/trafficandcommuting/fem... [2] https://en.wikipedia.org/wiki/No_free_lunch_theorem https://en.wikipedia.org/wiki/No_free_lunch_theorem

gowld 9y ago

"Causation" is defined as temporal correlation: changes in A cause changes in B. There's no reason to thin computers would be "bad" at this. People are only bad at this because they have small, lazy brains Even the greatest physicists and mathematicians can't tell the difference between correlation and causation. When people say "causation is not correlation", what they truly mean is "spurious, context-sensitive correlation is not strong universal correlation"

joshuamorton 9y ago
No. Temporal correlation is "changes in A precede changes in B". This is not causation, you more or less correctly describe causation when you say "Changes in A cause changes in B", but a better way of putting it might be "all else equal, changes in A cause changes in B". The difficulty here is controlling for all possible C. As a simple example: If A causes a change in both B and C, but the change in B happens more quickly, "temporal correlation" would imply that B causes C, when that's not the case. This is especially obvious with cyclical phenomena. The tide going out does not cause the sun to rise, for example, even though I'm sure I could draw a temporal correlation between them. Nor does my car being in my garage cause me to go to work.