Timeline for A Bayesian perspective on omitted-variable bias (and other covariate-selection bias problems)
Current License: CC BY-SA 3.0
9 events
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| Mar 18, 2014 at 12:06 | vote | accept | Dr. Beeblebrox | ||
| Aug 12, 2013 at 14:50 | comment | added | ely | This is why you don't typically see many Bayesian methods being motivated because of their unbiasedness properties, and why you don't see a lot of effort placed on explicating the assumptions needed to make a method take on unbiasedness or efficiency properties. The Bayesian knows those assumptions wil never be satisfied, the model is misspecified, and so we can get on with more grown up things. | |
| Aug 12, 2013 at 14:48 | comment | added | ely | I agree. I'm just saying that Bayesian inference is not a priori concerned with unbiasedness properties of estimators. If such properties are useful for the substantive inference demanded by the applied problem, then of course it would make sense to try to get guarantees about unbiasedness. But if an inference procedure performs well without any particular good or bad properties related to its bias, a Bayesian will still use it while a frequentist may disregard it on a priori grounds that it doesn't provide a particular guarantee about unbiasedness. | |
| Aug 12, 2013 at 13:59 | comment | added | Manoel Galdino | I think there is one case where Bayesian may care about unbiasedness estimation: when estimating causal effects. See many papers by Rubbin. | |
| Aug 6, 2013 at 14:22 | history | edited | ely | CC BY-SA 3.0 |
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| Aug 6, 2013 at 13:53 | comment | added | ely | That is to say, if you're performing some inference for a goal or to answer a question, you (as the statistician) should be aware of the outcomes that matter and how they differ from one another. I suppose that could be a function of a simple error term, and in those applied inference problems you would care about getting an unbiased answer. In another problem where distinguishing between group-level effects was extremely important, and knowing the average effect within a group was a lot less important, you might choose models for completely different properties. | |
| Aug 6, 2013 at 13:51 | comment | added | ely | I could be mistaking, but my understanding is that the goal is to never break down the analysis along dimensions like MSE or other specified error terms. What you care about are the ultimate inferences that come from the model and how they inform the adoption of policies (where policies is broadly interpreted to be whatever beliefs or actions one should adopt as inferred from the data). This is a major distinction between frequentist education in statistics, which tends to focus on a quantity that can be optimized to identify a model, vs. Bayesian which wants a nuanced distribution over models | |
| Aug 6, 2013 at 13:32 | comment | added | Dr. Beeblebrox | Is it really that Bayesian estimation doesn't care very much about bias, or that it just doesn't care exclusively about bias? If a Bayesian approach focuses more on MSE than on bias, then, ceteris paribus, higher bias is undesirable. | |
| Aug 6, 2013 at 13:19 | history | answered | ely | CC BY-SA 3.0 |