Maybe I’m confused or misinterpreting. The first sentence of your first paragraph appears to contradict the first sentence of your second paragraph. The two claims seem incommensurable.
The first sentence of your first paragraph appears to appeal to experiment, while the first sentence of your second paragraph seems to boil down to “Classically, X causes Y if there is a significant statistical connection twixt X and Y.”
Several problems with this view of causality as deriving from stats. First, the nature of the statistical distribution makes a huge difference. Extreme outliers will occur much more frequency in a Poisson distribution, for instance, than in a Guassian distribution. It can be very hard to determine the nature of the statistical distribution you’re dealing with with any degree of confidence, particularly if your sample size is small.
For instance, we can’t really calculate or even estimate the likelihood of a large asteroid strike of the magnitude of the one that caused the Tertiary-Cretaceous boundary. These events occur too infrequently so the statistical power of the data is too low to give us any confidence.
The second problem is that any statistical estimate of connection between events is always vulnerable to the four horsemen of irreproducibility: HARKing, low statistical power (AKA too few data points), P-hacking (AKA the garden of forking paths), and publication bias.
The first sentence of the first paragraph claims “The standard account of causality depends on the idea of intervention...” Do you have any evidence to support this? Classicaly, arguments about causation seem be manifold and many don’t involve empirical evidence.
One classical criterion for causality, Occam’s Razor, involves the simplicity of the reasoning involved and makes no reference to empirical evidence.
Another classical criterion for causality involves the beauty of the mathematics involved in the model. This second criterion has been championed by scientists like Frank Wilczek and Paul Dirac, who asserted “A physical law must possess mathematical beauty,” but criticized by scientists like Albert Einstein, who said “Elegance is for tailors; don’t believe a theory just because it’s beautiful.”
Yet another classical criterion for causality involve Bayesian reasoning and the re-evaluation of prior beliefs. Older models of causality boil down to religious and aesthetic considerations—viz., Aristotle’s claim that planetary orbits must be circular because the circle is the most perfect gemoetric figure.
The first sentence of your first paragraph appears to appeal to experiment, while the first sentence of your second paragraph seems to boil down to “Classically, X causes Y if there is a significant statistical connection twixt X and Y.”
No. “Dependence” in that second sentence does not mean causation. It just means statistical dependence. The definition of dependence is important because an intervention must be statistically independent from things “before” the intervention.
None of these appear to involve intervention.
These are methods of causal inference. I’m talking about what causality is. As in, what is the difference between a mere correlation, and causation? The difference is that the second is robust to intervention: if X causes Y, then if I decide to do X, even in circumstances different from those where I’ve observed it before, Y will happen. If X only correlates with Y, it might not.
Maybe I’m confused or misinterpreting. The first sentence of your first paragraph appears to contradict the first sentence of your second paragraph. The two claims seem incommensurable.
The first sentence of your first paragraph appears to appeal to experiment, while the first sentence of your second paragraph seems to boil down to “Classically, X causes Y if there is a significant statistical connection twixt X and Y.”
Several problems with this view of causality as deriving from stats. First, the nature of the statistical distribution makes a huge difference. Extreme outliers will occur much more frequency in a Poisson distribution, for instance, than in a Guassian distribution. It can be very hard to determine the nature of the statistical distribution you’re dealing with with any degree of confidence, particularly if your sample size is small.
For instance, we can’t really calculate or even estimate the likelihood of a large asteroid strike of the magnitude of the one that caused the Tertiary-Cretaceous boundary. These events occur too infrequently so the statistical power of the data is too low to give us any confidence.
The second problem is that any statistical estimate of connection between events is always vulnerable to the four horsemen of irreproducibility: HARKing, low statistical power (AKA too few data points), P-hacking (AKA the garden of forking paths), and publication bias.
https://www.mrc-cbu.cam.ac.uk/wp-content/uploads/2016/09/Bishop_CBUOpenScience_November2016.pdf
The first sentence of the first paragraph claims “The standard account of causality depends on the idea of intervention...” Do you have any evidence to support this? Classicaly, arguments about causation seem be manifold and many don’t involve empirical evidence.
One classical criterion for causality, Occam’s Razor, involves the simplicity of the reasoning involved and makes no reference to empirical evidence.
Another classical criterion for causality involves the beauty of the mathematics involved in the model. This second criterion has been championed by scientists like Frank Wilczek and Paul Dirac, who asserted “A physical law must possess mathematical beauty,” but criticized by scientists like Albert Einstein, who said “Elegance is for tailors; don’t believe a theory just because it’s beautiful.”
Yet another classical criterion for causality involve Bayesian reasoning and the re-evaluation of prior beliefs. Older models of causality boil down to religious and aesthetic considerations—viz., Aristotle’s claim that planetary orbits must be circular because the circle is the most perfect gemoetric figure.
None of these appear to involve intervention.
No. “Dependence” in that second sentence does not mean causation. It just means statistical dependence. The definition of dependence is important because an intervention must be statistically independent from things “before” the intervention.
These are methods of causal inference. I’m talking about what causality is. As in, what is the difference between a mere correlation, and causation? The difference is that the second is robust to intervention: if X causes Y, then if I decide to do X, even in circumstances different from those where I’ve observed it before, Y will happen. If X only correlates with Y, it might not.