Brady Neal

@CasualBrady

PhD student in causal inference and machine learning at Causality Blog:

Vrijeme pridruživanja: svibanj 2010.

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  1. Prikvačeni tweet
    4. pro 2019.

    I made a flowchart to help people choose which causal inference book to read :)

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  2. proslijedio/la je Tweet
    21. sij

    An improved version of our can now be *freely* downloaded. Many thanks to all of you who reported typos and other issues. Scoop: This version includes the book cover illustrated by the inimitable Josh McKible ().

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  3. proslijedio/la je Tweet
    31. sij

    New article in . and review Cook & Campbell’s 'threats to validity’ translating each to epidemiology with DAGs and examples, bridging a gap between social science and epidemiologic research

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  4. proslijedio/la je Tweet
    23. sij

    A new dawn in sensitivity analysis! A systematic approach based on meaningful assumptions.

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  5. 23. sij

    The fundamental problem of inference is not always a problem. This is the case in simulations and computer programs. As models of the world get better, it becomes less and less of a problem in general.

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  6. proslijedio/la je Tweet
    23. sij

    The new ifo DICE Report Winter 2019 just came out with a chapter by me and Dirk Czarnitzki on "Innovation Policy and Causality". It even contains a, somewhat artsy, DAG (thanks to the layout team), plus it's pretty short (only four pages). So check it out!

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  7. proslijedio/la je Tweet
    10. sij

    I'm reading this extensive review of Fairness in Machine Learning and am happy to see that it acknowledges (though not strongly enough imo) that "fairness" is a causal notion, and that DAGs plus counterfactuals are needed to make sense of it.

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  8. proslijedio/la je Tweet
    9. sij

    The best blog post that I've read in a while. Thanks . :)

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  9. proslijedio/la je Tweet
    7. sij
    Odgovor korisnicima

    I'm not saying rebuttals don't sometimes sway things. But I'm certain that the field wastes a huge amount of effort mounting rebuttals that often go unread. I'd be more in favor of NEW reviewers coming in to assess whether the rebuttals have addressed concerns (doing AC's job).

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  10. 6. sij

    This was also the subject of a recent paper with , Aristide Baratin, , , , and (see blog post for details about this paper). [2/2]

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  11. 6. sij

    The way the bias-variance tradeoff is presented in textbooks can be misleading. Textbooks need an update. In this blog post, we cover a modern perspective on the bias-variance tradeoff, which was the subject of my Master's thesis. [1/2]

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  12. proslijedio/la je Tweet
    18. pro 2019.

    Finally, and I are able to share a working paper that we've been working on for quite a while now: "Causal Inference and Data-Fusion in Econometrics" 🚨 1/

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  13. 16. pro 2019.
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  14. proslijedio/la je Tweet
    7. pro 2019.

    I am looking for Masters and Ph.D. students to start in Fall 2020. If you are interested in working with me at (), the deadline is Dec 15. More info here: . You can also come to talk to me at .

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  15. proslijedio/la je Tweet

    Bernhard Scholkopf () just published a single author paper titled "Causality for Machine Learning" (); this should probably at the top of the reading list for many people interested in machine learning / AI;

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  16. proslijedio/la je Tweet
    16. stu 2019.

    Sharing a new refinement on "what is a cause", and "Is race a cause" and "why does it matter?"

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  17. 6. velj 2019.

    Happy to announce our recent paper: "A Modern Take on the Bias-Variance Tradeoff in Neural Networks" - with Sarthak Mittal, Aristide Baratin, , , , and

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  18. 9. ruj 2013.
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