[gov3009-l] Applied Stats - 3/4 - Maximilian Kasy
astrezhnev at fas.harvard.edu
Mon Mar 2 10:26:14 EST 2015
This week at the Applied Statistics Workshop we will be welcoming
Maximilian Kasy, an Assistant Professor of Economics at Harvard University.
He will be presenting work entitled *Why experimenters should not
randomize, and what they should do instead*. Please find the abstract
below and on the website
As usual, we will meet in CGIS Knafel Room 354 and lunch will be provided.
See you all there!
Title: Why experimenters should not randomize, and what they should do
Abstract: This paper discusses experimental design for the case that (i) we
are given a distribution of covariates from a pre-selected random sample,
and (ii) we are interested in the average treatment effect (ATE) of some
binary treatment. We show that in general there is a unique optimal
non-random treatment assignment if there are continuous covariates. We
argue that experimenters should choose this assignment. The optimal
assignment minimizes the risk (e.g., expected squared error) of treatment
effects estimators. We provide explicit expressions for the risk, and
discuss algorithms which minimize it. The objective of controlled trials is
to have treatment groups which are similar a priori (balanced), so we can
“compare apples with apples.” The expressions for risk derived in this
paper provide an operationalization of the notion of balance. The intuition
for our non-randomization result is similar to the reasons for not using
randomized estimators - adding noise can never decrease risk. The formal
setup we consider is decision-theoretic and nonparametric. In simulations
and an application to project STAR we find that optimal designs have mean
squared errors of up to 20% less than randomized designs and up to 14% less
than stratified designs.
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