Peng Ding (@pengding00) / X

Peng Ding

75 posts

Peng Ding

@pengding00

Professor of Statistics

UC Berkeley

sites.google.com/site/pengdingp…

Joined December 2022

Pinned
Peng Ding
@pengding00
Jul 27, 2024
excited to see the physical copy of the book; nervous about the potential errors. Comments are welcome.
Peng Ding
@pengding00
Oct 5, 2023
I just uploaded the R code and datasets to Harvard Dataverse: doi.org/10.7910/DVN/ZX… I plan to provide Python code as well but I need to learn Python first.
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Peng Ding
@pengding00
Oct 5, 2023
I just uploaded the R code and datasets to Harvard Dataverse: doi.org/10.7910/DVN/ZX… I plan to provide Python code as well but I need to learn Python first.
Giuseppe Cavaliere
@CavaliereGiu
Oct 4, 2023
Hi #EconTwitter! Interested in exploring #statistics for 𝐂𝐚𝐮𝐬𝐚𝐥 𝐈𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞? Don't miss👇the October 2023 edition of these outstanding undergrad notes by @pengding00 from @UCBStatistics. They are a great supplement to #econometrics books discussing difference in
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Peng Ding
@pengding00
May 31, 2023
My lecture notes on causal inference
Fang Han
@johnleibniz
May 31, 2023
Look at what I found on arXiv today 🤩 ⁦@pengding00⁩ arxiv.org/abs/2305.18793
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Peng Ding
@pengding00
Apr 25, 2024
Just gave a guest lecture on Bayesian Causal Inference at Williams College, with slides and R code at doi.org/10.7910/DVN/JO… which is an introduction to our review paper royalsocietypublishing.org/doi/10.1098/rs… @fabri_mealli @FanLiDuke (I never taught any Bayesian Statistics at Berkeley.)
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Peng Ding
@pengding00
Jan 4, 2024
I just posted my notes for Stat 230 ``Linear Models'' to ArXiv: arxiv.org/pdf/2401.00649… It covers the linear model and many extensions. I will teach it again in the spring and continue polishing the notes. Comments are welcome.
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Peng Ding
@pengding00
Aug 27, 2024
We view DID as a factorial design with the panel data structure. It is a fun paper. Comments are welcome.
Yiqing Xu
@xuyiqing
Aug 27, 2024
Sharing a new working paper with Anqi Zhao & Peng Ding @pengding00, titled "Factorial Difference-in-Differences." arxiv.org/pdf/2407.11937… 🧵 Comments and suggestions are welcome!
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Peng Ding
@pengding00
Oct 29, 2023
Z-bias is mysterious. I learned the intuition from Robins: ``If we adjust for the instrumental variable, the treatment variation is driven more by the unmeasured confounder, which could result in increased bias due to this confounder...'' in academic.oup.com/biomet/article… Section 1.
apoorva.lal
@Apoorva__Lal
Oct 29, 2023
The inverse of this is also interesting: adjusting for covariates that only predict X (thereby reducing Var(X̃)) increases β, thereby producing bias (known as Z-bias - @pengding00 notes:). Think back to facile comments in seminars about "but have you controlled for <something>".
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Peng Ding
@pengding00
Nov 1, 2023
This is amazing. Python code for ``A first course in causal inference'': arxiv.org/pdf/2305.18793… Thank you, Apoorva.
apoorva.lal
@Apoorva__Lal
Oct 31, 2023
Done with python code to accompany all chapters of @pengding00's textbook. IV and matching are not well supported in python, so I might spin those out into a package eventually. github.com/apoorvalal/din…
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Peng Ding
@pengding00
Jun 1, 2024
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/article…
Matt Blackwell
@matt_blackwell
May 31, 2024
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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Peng Ding
@pengding00
Jun 3, 2024
IPW with the estimated propensity score is another example. The first-stage estimation reduces the asymptotic variance, which surprises many people. A recent paper is arxiv.org/pdf/2303.17102 Also, Newey&McFadden chapter 6 is about "two-step estimation" sciencedirect.com/science/articl…
apoorva.lal
@Apoorva__Lal
Jun 3, 2024
Anyone have other examples of multi-step estimation problems where one needs to propagate uncertainty in first-step estimation into subsequent-stage coefficients? Generated regressors would be a standard example (eg centering regressors as in qt)
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Peng Ding
@pengding00
Oct 29, 2023
Dennis observed the numerical equivalence of the Horizontal and Vertical Regression in panel data @gitinmabellayo based on the OLS interpolator. Now we are interested in the OLS interpolator itself: arxiv.org/pdf/2309.15769…
Giuseppe Cavaliere
@CavaliereGiu
Oct 29, 2023
Hi #EconTwitter! 📈 Interested in causal inference based on panel data? Check out 👇this recent #econometrics paper (accepted in Econometrica) by Dennis Shen, Jasjeet Sekhon and @UCBerkeley statisticians @pengding00 & Bin Yu. They explore the merger of time series &
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Peng Ding
@pengding00
Mar 25, 2024
Hope we provide some new insights into the old problem of missing data in RCTs. @FanLiDuke
Fan Li
@FanLiDuke
Mar 25, 2024
Happy to see my paper with @pengding00 and Anqi Zhao @DukeU "Covariate adjustment in randomized experiments with missing outcomes and covariates" is out doi.org/10.1093/biomet… This 8-pages paper gives a simple and clean solution to a prevalent practical problem.
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Peng Ding
@pengding00
Nov 1, 2023
The bias-corrected matching estimator has the same form as the doubly robust estimator; see proposition 15.2 of arxiv.org/pdf/2305.18793… Zhexiao and Fang made the argument rigorous! @zzzxlin @johnleibniz
Zhexiao Lin
@zzzxlin
Oct 31, 2023
🤩Finally come!!! Very fortunate to be advised by Fang @johnleibniz and Peng @pengding00 on this paper.
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Peng Ding
@pengding00
Apr 19, 2024
Fan Li's slides for causal inference
Fan Li
@FanLiDuke
Apr 19, 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. www2.stat.duke.edu/~fl35/CausalIn…
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