Peng Ding
@pengding00
Followers
1,896
Following
402
Media
0
Statuses
42
Associate Professor of Statistics
Berkeley
Joined December 2022
Don't wanna be here?
Send us removal request.
Explore trending content on Musk Viewer
Supreme Court
• 819719 Tweets
राहुल गांधी
• 634054 Tweets
LINGORM TSOU EP2
• 590706 Tweets
SCOTUS
• 400645 Tweets
Hindu
• 394314 Tweets
Canada
• 359457 Tweets
Francia
• 303253 Tweets
Constitution
• 193911 Tweets
Presidents
• 152525 Tweets
Klay
• 144410 Tweets
#uelkemdesığınmacıistemiyorum
• 141599 Tweets
#FRABEL
• 131143 Tweets
Jack Smith
• 124100 Tweets
Arap
• 111231 Tweets
Warriors
• 110998 Tweets
Belgium
• 103171 Tweets
#suriyeliler
• 83314 Tweets
Dallas
• 76264 Tweets
سلمان
• 66956 Tweets
Belgique
• 60245 Tweets
Seal Team 6
• 52033 Tweets
فرنسا
• 51463 Tweets
Rick Ross
• 44756 Tweets
Saliba
• 41786 Tweets
Kounde
• 37227 Tweets
Lukaku
• 37090 Tweets
King Biden
• 35947 Tweets
Bélgica
• 32125 Tweets
Thuram
• 30617 Tweets
Kolo Muani
• 28406 Tweets
Griezmann
• 25527 Tweets
Tchouameni
• 25254 Tweets
Doku
• 22741 Tweets
Deschamps
• 22118 Tweets
De Bruyne
• 22096 Tweets
Rabiot
• 18350 Tweets
ベルギー
• 17203 Tweets
Josh Green
• 16913 Tweets
Hellboy
• 16448 Tweets
Demar
• 12394 Tweets
$CARTEL
• 11367 Tweets
كانتي
• 10311 Tweets
I just uploaded the R code and datasets to Harvard Dataverse: I plan to provide Python code as well but I need to learn Python first.
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
3
82
312
2
40
282
My lecture notes on causal inference
3
53
270
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.)
0
55
200
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.
2
40
174
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 Section 1.
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>".
3
20
125
6
16
132
This is amazing. Python code for ``A first course in causal inference'': Thank you, Apoorva.
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.
3
31
142
1
27
127
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
13
58
566
0
21
88
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)
12
2
27
0
8
74
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:
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 &
0
53
215
2
8
46
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.
1
31
94
0
3
41
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.
1
2
36
0
2
28
Fan Li's slides for causal inference
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.
11
142
640
0
0
24
``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.
1
0
2
0
0
21
@linstonwin
@Apoorva__Lal
@FelixThoemmes
@matt_blackwell
Also, the bootstrap is always a panacea to problems like this. But we need to center x in each bootstrap sample. If we center x before the bootstrap, we will get the EHW se again. I included this as a numerical exercise in Problem 9.2 of the book:
1
0
14
@jmwooldridge
I used the phrase ``population residual'' in Appendix B of I remember that
@stat110
did not like it when he read my early draft.
0
0
4
@AngYu_soci
In theory (and intuitively), if you include more terms in the logit pscore model, you can get better efficiency and potentially achieve the efficiency bound.
1
0
4
@Yiqiao_Zhong
@karlrohe
I think Gauss showed more: for OLS to be optimal, the noise must be Gaussian. The other way is easier to show and it is a current textbook result.
1
0
2
0
0
1
@yudapearl
You can download the PDF of it from the UCLA library. I just download it from the UC Berkeley library.
0
0
3
@pedrohcgs
This is too bad. I do not understand why some reviewers and editors want to waste everybody's time.
0
0
2
@Apoorva__Lal
Maybe you can try a simpler variance estimator in the first displayed formula on page 208 of It is equivalent to Otsu and Rai (2017)'s bootstrap variance estimator. But you do not need to bootstrap.
1
0
2
@Apoorva__Lal
@johnleibniz
@zzzxlin
This seems a hard question in practice. Maybe the best way is to display a sequence of estimates and se's with varying M.
0
0
1
@qsunstats
We used it for variance estimation because the in-sample residuals are 0: see section 5.3 of . .
1
0
1
@Apoorva__Lal
@RobDonnelly47
I drew the DAGs for M-bias and Z-bias using \xymatrix -:)
$$
\xymatrix{
U_1 \ar[dd]\ar[dr] &&U_2 \ar[dd]\ar[dl] \\
&X& \\
Z && Y
}
$$
and
$$
\xymatrix{
&&&U \ar[dl]_b \ar[dr]^c \\
X \ar[rr]^a &&Z\ar[rr]^\tau && Y
}
$$
0
0
1
0
0
1