Timeline for reverse causality and endogeneity problems
Current License: CC BY-SA 3.0
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| Mar 16, 2017 at 13:54 | comment | added | Neil G | @JulianSchuessler I have clarified my answer and quoted the relevant passage. I have also explained why trying to controlling for confounders is fruitless. In an example like the above question, you open up many more back doors than you close, which you then need to close, ad infinitum. | |
| Mar 16, 2017 at 13:50 | history | edited | Neil G | CC BY-SA 3.0 |
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| Mar 16, 2017 at 13:43 | history | edited | Neil G | CC BY-SA 3.0 |
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| Mar 16, 2017 at 13:36 | history | edited | Neil G | CC BY-SA 3.0 |
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| Mar 16, 2017 at 8:52 | comment | added | Julian Schuessler | This is a really weird answer. Nowhere does Pearl suggest you have to intervene on confounders, and conditioning on confounders may open back-door paths, but certainly also closes some (by the definition of confounders). Also, Pearl talks a lot about identification from observational data; in fact, the article you link clearly states that mediation effects CANNOT be identified from experimental data where one intervenes on treatment and mediator. | |
| Mar 16, 2017 at 6:53 | comment | added | Neil G | @DimitriyV.Masterov I did mention that you can try to close "back doors, but the validity of your conclusions will depend on your assumptions." That said, you should take a look at Pearl's paper. I'm not sure what you are randomizing, but if you're merely controlling for your confounders over random samples, you are fooling yourself with causal conclusions. | |
| Mar 16, 2017 at 5:19 | comment | added | dimitriy | There is are several approaches in econometrics and biostat that allow you to draw causal conclusions from observational data under some assumptions. The statement that you always need randomization is much too strong. | |
| Mar 16, 2017 at 4:52 | history | edited | Neil G | CC BY-SA 3.0 |
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| Mar 16, 2017 at 4:45 | history | answered | Neil G | CC BY-SA 3.0 |