Alexander D'Amour

@alexdamour

Research Scientist at Google Brain. Statistics, Data Science, ML, causality, fairness. Prev at Harvard (PhD), UC Berkeley (VAP). Opinions my own. he/him.

Cambridge, MA
alexdamour.com
Vrijeme pridruživanja: lipanj 2009.
44 fotografije ili videozapisa Fotografije i videozapisi

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  1. Prikvačeni tweet
    8. stu 2017.

    NEW on arXiv from me, Peng Ding, , , Jas Sekhon: Overlap in Observational Studies with High-Dimensional Covariates. Highlights a major often-overlooked concern when using Big Data™️ for causal inference.

    1 reply 20 proslijeđenih tweetova 57 korisnika označava da im se sviđa
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  2. proslijedio/la je Tweet
    30. sij

    Talk by at on "Fairness is not static: deeper understanding of long term fairness via simulation studies," with Hansa Srinivasan, , Pallavi Baljekar, D. Sculley, Yoni Halpern

    1 reply 4 proslijeđena tweeta 17 korisnika označava da im se sviđa
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  3. proslijedio/la je Tweet
    27. sij

    The 2020 Atlantic Causal Inference Conference (ACIC) will be in Austin, Texas, May 27-29. Submissions due Feb 7. This coincides with the conference being renamed "American" rather than "Atlantic"

    13 proslijeđenih tweetova 46 korisnika označava da im se sviđa
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  4. proslijedio/la je Tweet
    23. sij

    Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects by Fredrik D. Johansson et al. including ,

    17 proslijeđenih tweetova 38 korisnika označava da im se sviđa
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  5. proslijedio/la je Tweet
    21. sij

    I have post-doc openings in my lab. Do you want to use empirical methods, design experiments, and collaborate with both large and small tech firms for research and social impact? Have interest in ed tech, fin tech, charitable giving, and social sector effectiveness? Apply! 1/3

    5 replies 260 proslijeđenih tweetova 442 korisnika označavaju da im se sviđa
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  6. proslijedio/la je Tweet
    22. sij

    (1/10) Happy to announce that my paper with is officially out on JRSS-B! () In this paper, we develop new sensitivity analyses tools to precisely quantify how strong confounders need to be to overturn your research conclusions.

    72 proslijeđena tweeta 266 korisnika označava da im se sviđa
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  7. proslijedio/la je Tweet
    19. sij

    Hey people managers! Here's a free, practical tip for making your organization more inclusive. It's one of the easiest and most cost effective ways to increase representation of senior level underestimated folk, and improve retention. It has to do with "Stretch Opportunities."

    45 replies 1.160 proslijeđenih tweetova 3.486 korisnika označava da im se sviđa
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  8. proslijedio/la je Tweet
    20. sij

    Thread with thoughts on : It is worth setting the work we do at in its historical context and think about future perspectives:

    1 reply 34 proslijeđena tweeta 90 korisnika označava da im se sviđa
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  9. proslijedio/la je Tweet
    18. sij

    A thread of classifiers learning a decision rule. Dashed line is optimal boundary. Animations with by and . Logistic regression {stats::glm} with each class having normally distributed features. (1/n)

    14 replies 480 proslijeđenih tweetova 1.362 korisnika označavaju da im se sviđa
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  10. proslijedio/la je Tweet
    17. sij

    Do you work with ? ? Care for a trip to Paris? Send your proposal for my panel on Causal Inference and Social Network Analysis at :

    13 proslijeđenih tweetova 22 korisnika označavaju da im se sviđa
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  11. 16. sij

    They're teaching partial identification pretty early these days.

    My kindergartener brought this word problem home and I have to admit I’m stumped
    9 proslijeđenih tweetova 112 korisnika označava da im se sviđa
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  12. proslijedio/la je Tweet
    15. sij

    Check out a new study into how the uncertainty of models degrade with increasing dataset shift. Do the models become increasingly uncertain or do they become confidently incorrect? Learn all about it below!

    135 proslijeđenih tweetova 373 korisnika označavaju da im se sviđa
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  13. proslijedio/la je Tweet
    13. sij

    We cannot fix what we cannot measure! Thank you for funding my FAI proposal on *credible* fairness assessments and robustly fair algorithms: Proud+excited to be working with the amazing people at on this project.

    1 reply 5 proslijeđenih tweetova 38 korisnika označava da im se sviđa
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  14. proslijedio/la je Tweet
    13. sij

    "But too seldom is the question asked: how can AI help correct these disparities?" Hot off the press! Check out our new commentary "Treating Health Disparities with AI" w/ coauthors Shalmali Joshi +

    8 replies 55 proslijeđenih tweetova 149 korisnika označava da im se sviđa
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  15. proslijedio/la je Tweet
    12. sij

    Our variable importance paper is out in JMLR!! Often, many prediction models achieve high accuracy. A variable could be important to some models, but not others. We study how important a variable could be to *any* model (in a pre-specified class). (1/6)

    1 reply 12 proslijeđenih tweetova 69 korisnika označava da im se sviđa
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  16. proslijedio/la je Tweet
    9. sij

    We are organizing a workshop on Causal learning for Decision Making at along with , Jovana Mitrovic, , Stefan and . Consider submitting your work!

    6 replies 45 proslijeđenih tweetova 188 korisnika označava da im se sviđa
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  17. proslijedio/la je Tweet
    8. sij

    Running this thing tomorrow! Very apropos of recent Twitter conversations about causality, composability, and symbolic

    I running a 60-minute webinar blitz on causal modeling in machine learning code examples THIS Thursday. Ideal for applied and practitioners interested in connections between dense causal inference lit and ML tools practice.
    1 reply 3 proslijeđena tweeta 13 korisnika označava da im se sviđa
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  18. proslijedio/la je Tweet
    7. sij

    Any examples of using Superlearner with DML or other semi-parametric methods besides TMLE, and likewise, people using TMLE with other ML nuisance parameter estimators? Curious how much of accuracy of the combo can be attributed to one vs other.

    1 reply 1 proslijeđeni tweet 7 korisnika označava da im se sviđa
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  19. proslijedio/la je Tweet
    7. sij

    <Obligatory new year recruiting pitch> I'm hiring a research scientist for my team at Lyft. We work at the intersection of causal inference and machine learning, developing tools for optimal decision making. Looking for folks with strong stats and causal inference backgrounds 📈

    17 replies 109 proslijeđenih tweetova 433 korisnika označavaju da im se sviđa
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  20. proslijedio/la je Tweet
    28. pro 2019.

    My research group (located at Cornell Tech campus in NYC) is looking to recruit a postdoc to work on topics related to causal inference, fairness in ML, and sequential decision making (bandits+RL). Positions are renewable (1-2 years). Please retweet to spread the word. 🙏

    5 replies 134 proslijeđena tweeta 253 korisnika označavaju da im se sviđa
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  21. proslijedio/la je Tweet
    27. pro 2019.

    In the works since...2015? 2016?...We built a new algorithm to infer continental genetic ancestry from off-target NGS reads @ 1e-4 coverage, and used it to show that carrier testing guidelines based on self-reported ethnicity are miscalibrated.

    "Genetic-Ancestry Analysis on >93,000 Individuals Undergoing Expanded Carrier Screening Reveals Limitations of Ethnicity-Based Medical Guidelines"
    9 proslijeđenih tweetova 31 korisnik označava da mu se sviđa
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