Timeline for What is the relation between causal inference and prediction?
Current License: CC BY-SA 4.0
7 events
| when toggle format | what | by | license | comment | |
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| S Jan 12, 2019 at 19:38 | history | suggested | user82135 | CC BY-SA 4.0 |
A few improvements
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| Jan 12, 2019 at 17:52 | review | Suggested edits | |||
| S Jan 12, 2019 at 19:38 | |||||
| Jul 28, 2014 at 2:53 | comment | added | Neil G | Okay, I see your point that associational is the default and that causal models are "nested" in the sense that they are more powerful. The question is what is the difference between a causal model and regression or classification (an associational model). And the main difference is that: While you can do regression from causes to their effect, or from effects to some hypothetical cause; in a causal model, the relationships are directed (causes to effects). These directions are required to support interventional reasoning, which associational models cannot support. | |
| Jul 27, 2014 at 12:59 | comment | added | generic_user | Well, isn't associational basically the default? And wouldn't causal be nested within associational? I've never heard of anyone ever talk about an ''associational model'', except perhaps disparagingly in the case of one where the supposedly causal effects were confounded. | |
| Jul 20, 2014 at 19:47 | comment | added | Neil G | This answer neglects the difference between causal and associational models. | |
| Jun 10, 2013 at 13:45 | vote | accept | Tim | ||
| Apr 23, 2013 at 3:44 | history | answered | generic_user | CC BY-SA 3.0 |