Fan Li
@FanLiDuke
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Professor@Duke, Statistician, Data Scientist, Causal Inference researcher
Durham, NC, USA
Joined March 2024
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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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
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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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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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:
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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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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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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Brilliant strike back. I was compelled to google who Genc is.
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🧵 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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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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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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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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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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Great new paper on the controversial issue of causal interpretation of hazard ratio by the Yale Fan @FanLi90
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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?
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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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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Another great book draft (on linear models) from Peng Ding @pengding00. Clean, clear, and concise. Very up-to-date.
[Download 400-page PDF eBook] Linear Models in #MachineLearning and Statistical Learning: https://t.co/h6C025DHoS ————— #Mathematics #DataScience #LinearAlgebra #Statistics
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⚠️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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