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A mixture of identifields

What are two totally different sides of your personality that kind of came together to form who you are today? I've always had a love of nature that kind of gave me a sense of awe and spirituality even though I've never been comfortable talking about it. At the same time, I've also always been into science fiction of the space and time travel variety. These stories gave me kind of an intellectual sense of imagination about how limited our current social values might be compared to all the possibilities that could exist throughout all of time and space. Today, I realize that those two sides of me really fused to give me an intuition that there might be some values that cosmic and timeless rather than just socially constructed in our present moment. I believe this is the source of my beliefs that things like justice, equality, and fairness could be ideas that are fundamentally embedded in the fabric of reality, just waiting for our silly little human social understanding to really grasp. As musch as I want to move toward equality using the discourse we have on hand, I still feel like our very conceptions of those ideas are still childishly rudimentary. I wonder how they'll look to some extraterrestrial species that has no sense of time. Maybe they can help us understand those things that it coulde take the span of a whole universe to figure out for sure.

... read more on bonkerfield.org

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>Adding it to the design matrix in the linear regression is equivalent to just estimating two of them, there are no spill-over effects. I guess in the linear case the only benefits are that you estimate the X coefficients on all the data and you are only fitting 1 intercept term to all the data. So it's probably pretty equivalent to fitting two models, it's just odd that I don't think I've ever seen anyone do it that way in their methods. Thanks for the guidance.

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You are absolutely right on the first part. Good point, we'd actually be formulating it with an IPTW weighted average. I figured I'd just leave that out of this for simplicity in this question, but you are right that it doesn't make sense just as an average. But for your second point, yes getting the expected difference in outcome conditional on X is a good way to frame it too. Would there be a name for fitting two completely different regression functions and taking the difference? u/[ExcelsiorStatistics](https://www.reddit.com/user/ExcelsiorStatistics/) answer made sense to me, in both the linear case as well as fitting an arbitrary function. If you include terms for X and A\*X in any function f(X,A\*X) (linear or nonlinear) then I'd think the model should benefit from joint fitting if there is any useful information and there is limited data.

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Ah yes, that actually makes a lot of sense. Then you would have the advantage of using all of your data for determining the intercept and X coefficients. Thank you.

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[Q] Is there a name for using the difference between separate regression models for control and treatment to estimate causal effects. from r/statistics

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Thoughts on how there are three ways to be a leftist, and how to keep them in balance. from r/alltheleft

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