
Peng Ding
@pengding00
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Associate Professor of Statistics
Berkeley
Joined December 2022
I just sent the new version of the textbook to CRC: with R code and data at: I can still make minor changes. Comments are welcome.
I just posted my notes for Stat 230 ``Linear Models'' to ArXiv: 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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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"):
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excited to see the published version of our paper on dealing with missing covariates and outcomes in randomized trials: It is a simple paper with some intriguing results. Slides are here: @FanLiDuke.
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RT @linstonwin: 101 years ago, Neyman introduced potential outcomes and design-based inference. For a special issue of Journal of Causal In….
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We view DID as a factorial design with the panel data structure. It is a fun paper. Comments are welcome.
Sharing a new working paper with Anqi Zhao & Peng Ding @pengding00, titled "Factorial Difference-in-Differences." 🧵. Comments and suggestions are welcome!
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RT @BerkeleyDataSci: 🧑🏫 UC Berkeley seeks applicants for 4 tenure-track and 1 tenured professor position in "AI, Inequality, and Society."….
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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 Also, Newey&McFadden chapter 6 is about "two-step estimation"
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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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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RT @ssrc_org: In @AmstatNews JASA, Anqi Zhao & @pengding00 consider alternative strategies to address covariate missingness in randomized e….
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Just gave a guest lecture on Bayesian Causal Inference at Williams College, with slides and R code at which is an introduction to our review paper @fabri_mealli @FanLiDuke (I never taught any Bayesian Statistics at Berkeley.).
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Hope we provide some new insights into the old problem of missing data in RCTs. @FanLiDuke.
Happy to see my paper with @pengding00 and Anqi Zhao @DukeU "Covariate adjustment in randomized experiments with missing outcomes and covariates" is out This 8-pages paper gives a simple and clean solution to a prevalent practical problem.
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``control'' means many different things in statistics, e.g. treatment-control experiment; control for confounding; case-control study; negative control; controlled direct effect; control function. ``control'' can even mean covariates (good or bad controls).
@pengding00 @carolcaetanoUGA May I ask what does “control” mean? I feel people usually just call it potential outcome. “Control” sounds like a mediation terminology.
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RT @bbiinnyyuu: My co-author @rlbarter and I are thrilled to announce the online release of our MIT Press book "Veridical Data Science: The….
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RT @ecmaEditors: How should we analyze experiments that randomly form groups of people? A new paper by Basse, @pengdingpku, @AviFeller, and….
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RT @TilburgEOR: Congratulations to Denis Kojevnikov who received the 2023 @JEconometrics Zellner Award, together with @vadimmarmer & Kyungc….
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RT @fperez_org: Open rank teaching professor position in EECS/Data Science at @BerkeleyDataSci!. Our teaching professors are in the Academi….
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The bias-corrected matching estimator has the same form as the doubly robust estimator; see proposition 15.2 of Zhexiao and Fang made the argument rigorous! @zzzxlin @johnleibniz.
🤩Finally come!!! Very fortunate to be advised by Fang @johnleibniz and Peng @pengding00 on this paper.
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