[YPNG] YPNG, Friday 10 February 2017

[Sekhar Tatikonda]

Hi Folks,

This Friday, Winston Lin will talk on:

"Agnostic Notes on Regression Adjustments to Experimental Data:
Reexamining Freedman’s Critique"

This talk will be mostly based on my 2013 Annals of Applied Statistics
paper, which reexamines David Freedman's critique of ordinary least
squares regression adjustment in randomized experiments.

Random assignment is intended to create comparable treatment and
control groups, reducing the need for dubious statistical models.
Nevertheless, researchers often use linear regression models to adjust
for random treatment-control differences in baseline characteristics.
The classic rationale, which assumes the regression model is true, is
that adjustment tends to reduce the variance of the estimated
treatment effect. In contrast, Freedman used a randomization-based
inference framework to argue that under model misspecification, OLS
adjustment can lead to increased asymptotic variance, invalid
estimates of variance, and small-sample bias. My paper shows that in
sufficiently large samples, those problems are either minor or easily
fixed. Neglected parallels between regression adjustment in
experiments and regression estimators in survey sampling turn out to
be very helpful for intuition.

Winston Lin (Ph.D., Statistics, UC Berkeley, 2013) is an adjunct
associate research scholar in the Department of Political Science at
Columbia University.


See you Friday at 11am in the Stat's classroom.

Regards,
sekhar
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