Hi all,
This week at the Applied Statistics workshop we will be welcoming Matt Taddy, a Professor
of Econometrics and Statistics at the University of Chicago Booth School of Business. He
will be presenting work entitled "Counterfatual Prediction with Deep Instrumental
Variables Networks." Please find the abstract below and on the website. The paper
can be found here:
https://arxiv.org/abs/1612.09596
We will meet in CGIS Knafel Room 354 at noon and lunch will be provided.
Best,
Pam
Title: Counterfactual Prediction with Deep Instrumental Variables Networks
(Jason Hartford, Greg Lewis, Kevin Leyton-Brown, Matt Taddy)
Abstract: We are in the middle of a remarkable rise in the use and capability of
artificial intelligence. Much of this growth has been fueled by the success of deep
learning architectures: models that map from observables to outputs via multiple layers of
latent representations. These deep learning algorithms are effective tools for
unstructured prediction, and they can be combined in AI systems to solve complex automated
reasoning problems. This paper provides a recipe for combining ML algorithms to solve for
causal effects in the presence of instrumental variables -- sources of treatment
randomization that are conditionally independent from the response. We show that a
flexible IV specification resolves into two prediction tasks that can be solved with deep
neural nets: a first-stage network for treatment prediction and a second-stage network
whose loss function involves integration over the conditional treatment distribution. This
Deep IV framework imposes some specific structure on the stochastic gradient descent
routine used for training, but it is general enough that we can take advantage of
off-the-shelf ML capabilities and avoid extensive algorithm customization. We outline how
to obtain out-of-sample causal validation in order to avoid over-fit. We also introduce
schemes for both Bayesian and frequentist inference: the former via a novel adaptation of
dropout training, and the latter via a data splitting routine.