In general: manipulate SMOKING in a way that’s not correlated with LESION; then see what happens to CANCER.
Example 1: randomly assign people to forced smoking / forced abstinence; check cancer rates in each group. (“controlled experimentation”)
Example 2: modulate smoking via tobacco taxes and see what happens to cancer rates. (Assuming that taxation levels are not correlated with lesion prevalence, you can even use merely observational data for this. Even if taxation is “reactive”—i.e. more lesions cause more smoking causes higher taxes causes less smoking—a “positive” result of cancer correlating with smoking will be informative.)
“assume that two factors outside of the current data set are unrelated, and proceed from there”
How would you rule out that taxes cause lesions, or that taxes cause cancer? How would you show that taxes reduce smoking, given only that higher taxes are a leading indicator of reduced smoking?
How would you rule out that taxes cause lesions, or that taxes cause cancer? How would you show that taxes reduce smoking, given only that higher taxes are a leading indicator of reduced smoking?
At some point, you have to bring in domain knowledge of causation. How do you know that pushing on the accelerator will make the car go faster?
One can think up scenarios in which taxes do indirectly cause cancer (or lesions): higher tax on tobacco --> people continue to use tobacco but switch to some cheaper, less preferred foodstuffs --> among which is one that unknown to anyone yet causes cancer (or lesions). But the first of those links is observable. If they don’t change their diet but do reduce their tobacco consumption, this chain is refuted.
For taxes causing reduced smoking, you could look at temporal relations, or ask people why they’ve cut down. If you find yourself inventing reasons why no-one can detect the dragon in your garage, at some point you have to accept that there is no dragon.
Evenly divide all people into two groups, and apply higher taxes to one group.
Changing taxes for everyone fails to test for a common cause of tax changes and smoking.
I can track a solid determimistic relationship from the accelerator pedal to the drivetrain. Deterministic cause and effect is easy to test. Stochastic cause and effect, much less so, especially with unknown confounding factors.
In general: manipulate SMOKING in a way that’s not correlated with LESION; then see what happens to CANCER.
Example 1: randomly assign people to forced smoking / forced abstinence; check cancer rates in each group. (“controlled experimentation”)
Example 2: modulate smoking via tobacco taxes and see what happens to cancer rates. (Assuming that taxation levels are not correlated with lesion prevalence, you can even use merely observational data for this. Even if taxation is “reactive”—i.e. more lesions cause more smoking causes higher taxes causes less smoking—a “positive” result of cancer correlating with smoking will be informative.)
“assume that two factors outside of the current data set are unrelated, and proceed from there”
How would you rule out that taxes cause lesions, or that taxes cause cancer? How would you show that taxes reduce smoking, given only that higher taxes are a leading indicator of reduced smoking?
At some point, you have to bring in domain knowledge of causation. How do you know that pushing on the accelerator will make the car go faster?
One can think up scenarios in which taxes do indirectly cause cancer (or lesions): higher tax on tobacco --> people continue to use tobacco but switch to some cheaper, less preferred foodstuffs --> among which is one that unknown to anyone yet causes cancer (or lesions). But the first of those links is observable. If they don’t change their diet but do reduce their tobacco consumption, this chain is refuted.
For taxes causing reduced smoking, you could look at temporal relations, or ask people why they’ve cut down. If you find yourself inventing reasons why no-one can detect the dragon in your garage, at some point you have to accept that there is no dragon.
Evenly divide all people into two groups, and apply higher taxes to one group.
Changing taxes for everyone fails to test for a common cause of tax changes and smoking.
I can track a solid determimistic relationship from the accelerator pedal to the drivetrain. Deterministic cause and effect is easy to test. Stochastic cause and effect, much less so, especially with unknown confounding factors.
Best of luck getting that one to fly in practice.
That’s the general problem with instrumental variables. But sometimes that’s all you have to work with.
That is the fundamental problem of statistical experiments.
Well, it’s just as easy as varying smoking directly, which would also work to identify a smoking->cancer effect.
Without experimentation, there is no way to distinguish between the world of smoker’s lesion and the world of carcinogenic cigarettes.