The question orienting work this week is here.
The general plan this term will be for the students to engage with one question per week.
The question orienting work this week is here.
The general plan this term will be for the students to engage with one question per week.
So, you want to compare groups (intervention units) who are similar on baseline but who differ on the intervention. Here is a very very quick and dirty overview of going from data to an estimate of the ATE (taken in part from the documentation for optmatch see also Mark Fredrickson’s work through.)
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You can then add the matching-factors to the data for later analysis:
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For example, you could estimate an ATE with lm:
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And you could approximate a randomization standard error for the ATE using an HC2 correction Winston Lin’s 2013 paper:
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Here is my hack to get an HC2 CI:
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Say have lots of .tex files that you’ve been processing with latexmk and your directory is littered with .aux, .log, etc.. files. Try:
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Say you want to rename a bunch of files from A_slides.tex A_handouts.tex A_slides_withmynotes.tex to filenames all beginning with B:
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