Fan Li Profile
Fan Li

@FanLiDuke

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Professor@Duke, Statistician, Data Scientist, Causal Inference researcher

Durham, NC, USA
Joined March 2024
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@FanLiDuke
Fan Li
2 years
Done another semester teaching causal inference🙂. Updated my course slides, added survival data, labs, corrected more typos this time. Close to 800 pages now. Always more to update next year. https://t.co/Bpy7uYRKVq
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@FanLiDuke
Fan Li
10 months
I was often asked by practitioners about power calculations for causal inference with observational data, a hard problem with little leads. Finally had a clean solution, thanks to my spectacular student Bo Liu. Here it is: https://t.co/r2nL9kTQ55 https://t.co/G077LDdG2Z
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cran.r-project.org
Sample size calculations in causal inference with observational data are increasingly desired. This package is a tool to calculate sample size under prespecified power with minimal summary quantities...
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@FanLiDuke
Fan Li
1 year
Hands-down the best textbook for causal inference.
@pengding00
Peng Ding
1 year
excited to see the physical copy of the book; nervous about the potential errors. Comments are welcome.
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@FanLiDuke
Fan Li
1 year
Twitter academia: 1. I am happy to announce xx (whatever trivial) 2. I am thrilled/excited that xx (papers, grants, promotion) 3. I am honored that xx ("awards" in all senses) Adding to that list now: "How I publish xx papers in x years" What next? How I become god?
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@pengding00
Peng Ding
1 year
This is an interesting and useful trick. However, centering factors has some special restrictions on the estimated factorial effects when there are more than 3 factors (3 is the magic number there!). This motivates us to write this paper:
@matt_blackwell
Matt Blackwell
1 year
A fun fact about regression that many know but maybe is new to you: If you have an interaction bw continuous X1 and binary X2, mean-centering X1 will make the coefficient on X2 be its marginal effect when X1 is at its mean level rather than 0 without changing the interaction
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@Apoorva__Lal
apoorva.lal
1 year
what a vote of confidence [cc @pengding00 ]
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@HOS_ASA
ASA History of Statistics Special Interest Group
1 year
The word ‘algorithm’ is surprisingly old. It was derived from ‘algorizmi’, the Latinised surname of the mathematician Abu Ja'far Mohammed Ben Musa Al-Khwarizmi (c. 780 - c.850). His writings were translated into Latin by Robert of Chester in the 12th c. 1/3
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@PhD_Genie
PhD_Genie
1 year
The added value of the literature review section https://t.co/IV4O4CocNG
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@FanLiDuke
Fan Li
1 year
Good stat joke. Once in a party, the late Susie Bayarri (one of the great Bayesians) and a few of us were discussing who is the statistician divorced the most times. Susie said: "He must be a frequentist!"
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@FanLiDuke
Fan Li
1 year
Brilliant strike back. I was compelled to google who Genc is.
@Toffeemen68
Ian P. McCarthy
1 year
The peer review process.
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@JAMA_current
JAMA
1 year
🧵 New Special Communication examines drawing causal inferences about the effects of interventions from observational studies in medical journals and suggests a framework that might be used. https://t.co/2KoFti2nQl
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@ssrc_org
SSRC
1 year
In @AmstatNews JASA, Anqi Zhao & @pengding00 consider alternative strategies to address covariate missingness in randomized experiments and recommend including missingness indicators when estimating average treatment effects. https://t.co/NHKKbSaItH
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@AdamMGrant
Adam Grant
4 years
Writing is more than a vehicle for communicating ideas. It's a tool for crystallizing ideas. Writing exposes gaps in your knowledge and logic. It pushes you to articulate assumptions and consider counterarguments. One of the best paths to sharper thinking is frequent writing.
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@FanLiDuke
Fan Li
2 years
Saw many cicada shells everywhere lately. Turned out two different broods of cicadas (one on a 13 yr and other on a 17 yr cycle -- two prime numbers) emerge at the same time from underground this year, first time since 1803, next time? 2024+17X13=2245.
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@cremieuxrecueil
Crémieux
2 years
Peer reviewed publications overstate how well anti-depressants work because the published literature omits lots of conflicting results🧵 When the FDA does their reviews, they notice lots of unpublished studies that tend to show the drugs are less effective.
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@FanLiDuke
Fan Li
2 years
Great new paper on the controversial issue of causal interpretation of hazard ratio by the Yale Fan @FanLi90
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@FanLiDuke
Fan Li
2 years
Excellent point. Serving as the editor for Social Science, Biostatistics, Policy at Annals of Applied Statistics, I have spent countless hours reviewing papers, burned social capitals, offending many authors (rejecting their papers). My stipend? 0. Is the system sustainable?
@jasonmfletcher
Jason Fletcher
2 years
I am honored to be offered the job of Editor-in-Chief of Health Economics @HECJournalTweet, replacing the excellent Sally Stearns (@GillingsGlobal). I plan to reject this offer. Why?
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@CavaliereGiu
Giuseppe Cavaliere
2 years
Hi #EconTwitter! 📈 Curious about the Bayesian take on causal inference? If so, you should check out the material from @FanLiDuke's (@DukeU) "Bayesian Causal Inference" course, along with the review paper by @FanLiDuke and @fabri_mealli (@UNI_FIRENZE)! 📚 They carefully
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@FanLiDuke
Fan Li
2 years
Another great book draft (on linear models) from Peng Ding @pengding00. Clean, clear, and concise. Very up-to-date.
@KirkDBorne
Kirk Borne
2 years
[Download 400-page PDF eBook] Linear Models in #MachineLearning and Statistical Learning: https://t.co/h6C025DHoS ————— #Mathematics #DataScience #LinearAlgebra #Statistics
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@kaidi_wu
Kaidi Wu, Ph.D.
2 years
⚠️For PhDs who are thinking about jumping ship and diving into industry: Industry isn't necessarily better than academia. I have straddled both worlds. Here are 5 MYTHS about academia vs. industry:
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