Weihan Zhang ㌠ @weihanzhang0427

Interested in causal inference and semiparametric efficiency theory. Biostatistics M.A. student @UCBerkeleySPH. Alumni @PKU_SG. ガルパンおじさん。米国留学中。学園艦 Joined September 2022

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STEINER軍曹 @STEINER1941

a week ago

10年前の秋葉原

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Weihan Zhang ㌠ @weihanzhang0427

4 weeks ago

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Weihan Zhang ㌠ @weihanzhang0427

4 weeks ago

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ジブリのせかい【非公式ファンサイト】 @ghibli_world

a month ago

◎39年前の本日 1986年8月2日に宮﨑駿監督の『天空の城ラピュタ』が公開。スタジオジブリの第一回作品です。現在では多くの人に愛され、映画史に残るような作品となりましたが、公開当時は観客動員数は77万人、配給収入5億8300万円と興行的には当たっていません。

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Third, and perhaps most shockingly, as a result of the deterioration of government finances, the decline of university funding is substantially more serious than what I heard in January, when I last visited. Back then, people at Peking and Tsinghua thought they were facing a…

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CTML - Center for Targeted Machine Learning @BerkeleyCTML

2 months ago

CTML VSR alum Carlos Meixide and CTML co-director Mark van der Laan present a new approach to causal inference. The new paper challenges the traditional assumption-based identification to what can be learned directly from observed data using implied interventions. Link in bio!🔗

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CTML - Center for Targeted Machine Learning @BerkeleyCTML

a month ago

CTML GSRs Wenxin Zhang, Kaiwen Hou, and Alissa Gordon will be presenting their work next week at the 2025 JSM Conference. Innovations in causal inference, adaptive study design, and statistical methodology will be highlighted. If you’re attending JSM–come show your support!

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Weihan Zhang ㌠ @weihanzhang0427

2 months ago

#PCIC2025

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オーバーフロー公式 @Overflow_staff

3 months ago

ちなみに、「好きな子の写真を撮って～」というのは当時の女の子にあったおまじないを男の子に転化したもので、こうした行為が盗撮扱いになった初のケースが2006年。スクールデイズは2005年のゲームです。20年で「オ゛ァ」と思うくらい倫理が変化していることがわかります。

えこもげ(Ecomoesty) @Oh_ec0m0esty

3 months ago

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𓆩♱ Iwaly @1Iwaly

4 months ago

#沙耶の唄

₊ ✚ bugbug ✚ ₊ @bugbug000

4 months ago

#沙耶の唄 https://t.co/l0mk5Npq26

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Rui Wang 王瑞 @RuiWang97

4 months ago

Our recent preprint titled “GMM with many weak moment conditions and nuisance parameters: General theory and applications in causal inference” is available on Arxiv now. We give two IV examples and one proximal causal inference example in the paper.

arXiv math.ST Statistics Theory @mathSTb

4 months ago

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Berkeley Statistics @UCBStatistics

4 months ago

As part of @UCBStatistics 70th anniversary, this Friday, Distinguished Professor Emeritus and Professor of the Graduate School, Peter Bickel, and Assistant Professor Will Fithian will talk at a Special Seminar Honoring legendary Professor David Blackwell. #BerkeleyStats

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Peng Ding @pengding00

5 months ago

very excited to see the paper "Nonparametric identification is not enough, but randomized controlled trials are" with comments from @beenwrekt ("A Bureaucratic Theory of Statistics") and myself ("What randomization can and cannot guarantee"): muse.jhu.edu/issue/54591

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Harvard University @Harvard

5 months ago

“No government—regardless of which party is in power—should dictate what private universities can teach, whom they can admit and hire, and which areas of study and inquiry they can pursue.” - President Alan Garber hrvd.me/GarberRespond3…

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Math Cafe @Riazi_Cafe_en

6 months ago

UC Berkeley's "Theoretical Statistics" PDF: stat.berkeley.edu/~aditya/resour…

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Edward Kennedy @edwardhkennedy

5 years ago

A short thread: It amazes me how many crucial ideas underlying now-popular semiparametrics (aka doubly robust parameter/functional estimation / TMLE / double/debiased/orthogonal ML etc etc) were first proposed many decades ago. I think this is widely under-appreciated!