[gov3009-l] Applied Statistics Workshop: Luke Miratrix on Wed., Sept. 26

Konstantin Kashin kkashin at fas.harvard.edu
Mon Sep 24 11:49:08 EDT 2012


*Dear all,

Please join us for the Applied Statistics Workshop (Gov 3009) this
Wednesday, September 26 from 12.00 - 1.30 pm in CGIS Knafel Room 354. Luke
Miratrix, Assistant Professor of Statistics in the Department of Statistics
at Harvard University, will give a presentation entitled "Random Weight
Estimators: Adjusting Randomized Trials Without Using Observed Outcomes". As
always, a light lunch will be provided.

Abstract:*

To increase the precision of a randomized trial, experimenters often adjust
> estimates of treatment effects using baseline covariates thought to predict
> the outcome of interest. In a previous paper, we proved that even under the
> Neyman-Rubin model, if the covariates and the method for adjustment are
> determined before randomization, this process can increase precision in a
> manner quite similar to a comparable blocked experiment. Typically,
> however, experimenters wish to adjust for the covariates that are most
> imbalanced between treatment and control, given the realized randomization.
> This leads to a much vexed variable selection problem that depends on the
> observed treatment assignment. To understand the issues behind this
> process, we examine a class of estimators we call "Random Weight
> Estimators" that adjust treatment effect estimates by weighting units with
> weights depending on a function on treatment assignment and covariates.
> While similar in spirit to blocking, these estimators can be applied "after
> the fact,'' i.e., after randomization has occurred, allowing them to
> naturally adapt to the observed treatment assignment. They can also adjust
> for many different covariates at once, including continuous ones. This
> class is quite general, and it includes traditional methods such as
> ordinary linear regression. Using our framework, we show, under the
> Neyman-Rubin model, how one can easily introduce potential bias using what
> would seem to be legitimate and simple approaches, especially in small and
> midsize experiments. Care must be taken with many forms of adjustment, even
> if an approach is selected without regard to any actual outcomes. We also
> extend this methodology to survey experiments, giving an appropriate and
> near-unbiased estimator for the treatment effect of a parent population.
> Throughout the talk, we illustrate this overall framework.

**
**
An up-to-date schedule for the workshop is available at
http://www.iq.harvard.edu/events/node/1208.
**
**
Best,
Konstantin

-- 
Konstantin Kashin
Ph.D. Candidate in Government
Harvard University

Mobile: 978-844-0538
E-mail: kkashin at fas.harvard.edu
Site: http://www.konstantinkashin.com/<http://people.fas.harvard.edu/%7Ekkashin/>
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