[gov3009-l] Zajonc on "Bayesian Inference for Dynamic Treatment Regimes"

Matt Blackwell mblackwell at iq.harvard.edu
Mon Mar 8 12:44:20 EST 2010


We hope you will join us this Wednesday, March 10th at the Applied
Statistics workshop when we will be happy to have Tristan Zajonc
(Harvard Kennedy School). Details and an abstract are below. A light
lunch will be served. Thanks!

"Bayesian Inference for Dynamic Treatment Regimes"
Tristan Zajonc
Harvard Kennedy School
March 10th, 2010, 12 noon
K354 CGIS Knafel (1737 Cambridge St)

Policies in health, education, and economics often unfold sequentially
and adapt to developing conditions. Doctors treat patients over time
depending on their prognosis, educators assign students to courses
given their past performance, and governments design social insurance
programs to address dynamic needs and incentives. I present the
Bayesian perspective on causal inference and optimal treatment choice
for these types of adaptive policies or dynamic treatment regimes. The
key empirical difficulty is dynamic selection into treatment:
intermediate outcomes are simultaneously pre-treatment confounders and
post-treatment outcomes, causing standard program evaluation methods
to fail. Once properly formulated, however, sequential selection into
treatment on past observables poses no unique difficulty for
model-based inference, and analysis proceeds equivalently to a
full-information analysis under complete randomization. I consider
optimal treatment choice as a Bayesian decision problem. Given data on
past treated and untreated units, analysts propose treatment rules for
future units to maximize a policymaker's objective function. When
policymaker’s have multidimensional preferences, the approach can
estimate the set of feasible outcomes or the tradeoff between equity
and efficiency. I demonstrate these methods through an application to
optimal student tracking in ninth and tenth grade mathematics. An easy
to implement optimal dynamic tracking regime increases tenth grade
mathematics achievement 0.1 standard deviations above the status quo,
with no corresponding increase in inequality. The proposed methods
provide a flexible and principled approach to causal inference for
sequential treatments and optimal treatment choice under uncertainty.


Matthew Blackwell
PhD Candidate
Institute for Quantitative Social Science
Department of Government
Harvard University
email: mblackwell at iq.harvard.edu
url: http://people.fas.harvard.edu/~blackwel/

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