parametric standard error post-matching #207
Replies: 10 comments
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Please add more details to this issue. Thanks. |
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@daniellebraun, @m-qin, @fbargaglistoffi, please add more details to this issue. |
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I think Danielle and Falco understand this statistical issue better than me, but my impression is that it centers around how we understand the In the SGI project, we expanded out the data (I forget what this is called) using |
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The variances estimates of the matching estimator are of interest. Importantly, a paper under binary treatment (https://www.tandfonline.com/doi/full/10.1080/01621459.2020.1840383) may illustrate some points raised here. But let's start simply. that is 1) if using a GLM in R, and set weight = counter_weight, I thought the GLM wouldn't treat two identical units independently, rather, they will use a weighted LS and corresponding weighted covariance matrix to calculate the variance. I do not think in this case, GEE will make difference. 2) If using a GLM in R on unweighted data (with many duplicated rows. and GLM will wrongly treat them independently), then a GEE is necessary. |
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If someone wants to write a theoretical paper on variance estimation of matching estimators, I am up for it. |
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The paper that @wxwx1993 linked looks cool; it found that (for binary treatments), "We have shown that sandwich standard errors are not generally valid for the post-matching least squares estimator. In this section, we will demonstrate that, when matching is done without replacement, clustered standard errors (Liang and Zeger 1986; Arellano 1987) can be employed to obtain valid estimates of the standard deviation of post-matching regression coefficients. ... In this [later] section, we show that a clustered version of the nonparametric [block] bootstrap (Efron 1979) is also valid." |
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@Naeemkh maybe this thread should be under Discussion rather than Issue, haha. Seems like the methodology is still being developed |
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Thanks, @wxwx1993 and @m-qin. I will move it under discussions. |
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@wxwx1993 For our SGI project, we did get different results using 1) weighted GLM using |
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I mean, those two are different, for sure. There are different ways to incorporate weights, and the variance formulas differ. That is why I am curious about how they are different and also how they compare to bootstrap results. |
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hi @wxwx1993 and @Naeemkh,
post-matching when running erf parametric i think we need to change code so that we adjust for counter using GEE, otherwise we are underestimating the standard error (this came up with michelle q and falco, for SGI project).
@m-qin
@fbargaglistoffi
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