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Nick HK
@nickchk
Econ prof @SeattleU. Book The Effect theeffectbook.net out now! Check my pinned thread for all my projects. Substack nickchk.substack.com
Joined October 2010
Posts
  • Pinned
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    This is a thread of me QTing myself so I can pin all my posted-on-Twitter projects. Causal inference animated graphs:
    As requested, slower graphs! Also added a graph on collider bias, the webpage explanation helps there. These graphs are intended to show what standard causal inference methods actually *do* to data, and how they work. This is what controlling for a binary variable looks like:
    GIF
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    I've been getting used to gganimate and thought it would be useful to put together some illustrations of what various causal inference methods *actually do to data* and how they work. Here, for example, is what it means to control for a (binary) variable
    GIF
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    In regression, there are several things that students are eternally concerned about but are actually Just Fine: 1. Your coefficients don't need to be significant 2. Your R^2 doesn't need to be huge 3. Your predictors can be correlated 4. Your variables don't need to be normal
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    I feel like this is often not made clear enough in metrics
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    My students have been having trouble figuring out how to put together a regression model - what to include, what to interact, which variables are left-hand/right hand. So I made a flowchart.
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    As requested, slower graphs! Also added a graph on collider bias, the webpage explanation helps there. These graphs are intended to show what standard causal inference methods actually *do* to data, and how they work. This is what controlling for a binary variable looks like:
    GIF
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    A graphical explanation of why you shouldn't use R^2 to pick your model.
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    Over winter break I'm planning to automate my CV, so I can just update a spreadsheet with accomplishments and it will spit out an updated website and PDF CV. Is this something anyone else would use or should I write it just for myself?
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    Today I'm releasing the first video designed to accompany my book The Effect, about research design and causal inference. There are ~70 videos planned for the series. These can be used to accompany the book, as classroom material, or just on their own.
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    Today I finally get to share the full draft of my new book The Effect. I think it turned out really well. The Effect offers a highly accessible and intuitive approach to research design and causal inference with observational data. Find it here: nickchk.com/causalitybook.…
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    My causal inference book The Effect is finally out today! It's a super intuitive look at causal research design, casual diagrams, and standard research methods. I'm super excited, and jealous of everyone getting their copies today before I get mine!
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    I'm excited to show off something I've been working on for a few months now: the materials from a new class that I pinpoints a very different, and I think very promising way of teaching undergrad econometrics (thread)
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    Last night my husband offered every trick or treater (old enough to handle it) a choice between a candy or a "mystery box". Every single child chose the mystery box. Trust levels high in my neighborhood.
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    I've written an undergraduate-friendly guide to using simulations to do power analysis. It's in R but the concepts are general. Suppose I could do a Stata version too. nickch-k.github.io/EconometricsSl…