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. stat.duke.edu/~fl35/CausalIn…

Try our new online calculator for sample size and power calculations for causal inference in observational studies, e.g. target trial emulation, which turns out to be simple -- only requiring two inputs besides the standard RCT inputs. stat.duke.edu/~fl35/pspower.…

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: arxiv.org/abs/2501.11181 cran.r-project.org/web/packa…

Hands-down the best textbook for causal inference.

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?

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: academic.oup.com/biomet/arti…

what a vote of confidence [cc @pengding00 ]

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

The added value of the literature review section

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!"

Brilliant strike back. I was compelled to google who Genc is.

🧵 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. ja.ma/3UxeAjL

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. tandfonline.com/doi/abs/10.1…

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.

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.

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.

Great new paper on the controversial issue of causal interpretation of hazard ratio by the Yale Fan @FanLi90

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?

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 present the Bayesian perspective on causal inference based on the potential outcomes framework. Highly recommended! ⭐️ Links: - Course: stat.duke.edu/~fl35/Bayesian… - Review paper: arxiv.org/abs/2206.15460

Another great book draft (on linear models) from Peng Ding @pengding00. Clean, clear, and concise. Very up-to-date.