[gov3009-l] Applied Stats - 3/25 - Fabrizia Mealli

Anton Strezhnev astrezhnev at fas.harvard.edu
Mon Mar 23 11:02:59 EDT 2015


Hi everyone!

This week at the Applied Statistics Workshop we will be welcoming *Fabrizia
Mealli*, Professor of Statistics, Informatics and Applications at the
University of Florence and Visiting Professor of Statistics at Harvard. She
will be presenting work entitled *Evaluating the effect of university
grants on student dropout: Evidence from a regression discontinuity design
using Bayesian principal stratification analysis*.  Please find the
abstract below and on the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/presentations/fabrizia-mealli-university-florence>
.

As usual, we will meet in CGIS Knafel Room 354 and lunch will be provided.
See you all there!

-- Anton

Title: Evaluating the effect of university grants on student dropout:
Evidence from a regression discontinuity design using Bayesian principal
stratification analysis

Abstract: Regression discontinuity (RD) designs are often interpreted as
local randomized experiments: a RD design can be considered as a randomized
experiment for units with a realized value of a so-called forcing variable
falling around a pre-fixed threshold. Motivated by the evaluation of
Italian university grants, we consider a fuzzy RD design where the receipt
of the treatment is based on both eligibility criteria and a voluntary
application status. Resting on the fact that grant application and grant
receipt statuses are post-assignment (post-eligibility) intermediate
variables, we use the principal stratification framework to define causal
estimands within the Rubin Causal Model. We propose a probabilistic
formulation of the assignment mechanism underlying RD designs, by
re-formulating the Stable Unit Treatment Value Assumption (SUTVA) and
making an explicit local overlap assumption for a subpopulation around
thethreshold. A local randomization assumption is invoked instead of more
standard continuity assumptions. We also develop a model-based Bayesian
approach to select the target subpopulation(s) with adjustment for multiple
comparisons, and to draw inference for the target causal estimands in this
framework. Applying the method to the data from two Italian universities,
we find evidence that university grants are effective in preventing
students from low-income families from dropping out of higher education.

Joint work with Fan Li and Alessandra Mattei
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