11 ms·

In general, no. Simple counterexample: everyday it rains my grass is wet, and everyday it doesn't rain the grass is dry. Does the rain cause the grass to be wet

by rm999 13y ago

In general, no. Simple counterexample: everyday it rains my grass is wet, and everyday it doesn't rain the grass is dry. Does the rain cause the grass to be wet? Well, probably. But maybe my grass is in a greenhouse, and I only turn on my sprinkler when it rains so the grass gets a "natural" amount of water. In this case the wet grass is not causally linked to the rain, even though it looks exactly like the situation where it is. My action of turning on the sprinkler is known as a "confounding factor". You need to make assumptions to be able to draw a causation from data. This is what people are doing when they design controlled experiments.

tedsanders 13y ago
I'm not an expert in algebraic statistics, but I think sometimes the answer is yes: http://acritch.com/media/math/critch-causality-talk-slides.pdf http://acritch.com/media/math/critch-causality-talk-slides.p... You have to assume the data you have is all that there is, and sometimes the math will only give a class of causal graphs instead of a single causal graph.
- rm999 13y ago
  From what I can tell, that presentation is assuming a certain class of graph structures in its DACB example - for example that there isn't an element E that can causally link the other nodes, emulating a causal relationship without there being one. Assuming a class of structures is not a general approach; in my example this would be the equivalent of assuming there is no sprinkler, but how can you make that assumption from the data alone? There's another issue: their approach is statistical, so even if you assume a class of graph structures you can only draw conclusions that the causal structure "almost surely" exists, not that it does exist. What this means is that even within infinite data, you can't prove that the other structures are impossible. If I turn on my sprinkler when it rains the first {very large number} times, you may conclude that there is almost surely a causal link between the rain and wet grass, but there is no reason I have to do it. You aren't left with a proof.
benched 13y ago
If you only turn on the sprinkler when it rains, the wet grass is still causally linked to the rain. There's just extra mediation - you.
- rm999 13y ago
  What you're saying insinuates I must turn on the sprinkler when it rains, which is not true. Just because I have my whole life doesn't mean I will for eternity. It's simply a correlation - I tend to turn on the sprinkler when it rains. The only causation is that the grass becomes wet when the sprinkler turns on.
  - benched 13y ago
    The only way this would have meaning for me would be if causation has a special, jargon meaning in this context that specifically means direct, deterministic causation.

by rm999 13y ago

tedsanders 13y ago

I'm not an expert in algebraic statistics, but I think sometimes the answer is yes: http://acritch.com/media/math/critch-causality-talk-slides.pdf http://acritch.com/media/math/critch-causality-talk-slides.p... You have to assume the data you have is all that there is, and sometimes the math will only give a class of causal graphs instead of a single causal graph.

rm999 13y ago
From what I can tell, that presentation is assuming a certain class of graph structures in its DACB example - for example that there isn't an element E that can causally link the other nodes, emulating a causal relationship without there being one. Assuming a class of structures is not a general approach; in my example this would be the equivalent of assuming there is no sprinkler, but how can you make that assumption from the data alone? There's another issue: their approach is statistical, so even if you assume a class of graph structures you can only draw conclusions that the causal structure "almost surely" exists, not that it does exist. What this means is that even within infinite data, you can't prove that the other structures are impossible. If I turn on my sprinkler when it rains the first {very large number} times, you may conclude that there is almost surely a causal link between the rain and wet grass, but there is no reason I have to do it. You aren't left with a proof.

benched 13y ago

If you only turn on the sprinkler when it rains, the wet grass is still causally linked to the rain. There's just extra mediation - you.

rm999 13y ago
What you're saying insinuates I must turn on the sprinkler when it rains, which is not true. Just because I have my whole life doesn't mean I will for eternity. It's simply a correlation - I tend to turn on the sprinkler when it rains. The only causation is that the grass becomes wet when the sprinkler turns on.
- benched 13y ago
  The only way this would have meaning for me would be if causation has a special, jargon meaning in this context that specifically means direct, deterministic causation.