Hi everyone!
This week at the Applied Statistics Workshop we will be welcoming *Finale
Doshi-Velez,* a Professor of Computer Science at the Harvard School of
Engineering and Applied Sciences. She will be presenting work entitled
*Bayesian
Or-of-And Models for Interpretable Classification*. Please find the
abstract below and on the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/presentations…>
.
As usual, we will meet in CGIS Knafel Room 354 and lunch will be provided.
See you all there!
-- Anton
Title: Bayesian Or-of-And Models for Interpretable Classification
Abstract: Interpretability is an important factor for models to be used and
trusted in many applications. Disjunctive normal forms, also known as
or-of-and models, are models with classification rules of the form "Predict
True if (A and B) or (A and C) or D." They are an appealing form of
classifier because one can easily trace how a classification decision was
made, and has some basis in human decision-making. In this talk, I will
talk about a Bayesian approach to learning or-of-and models and describe an
application to context-aware recommender systems.
This is joint work with Tong Wang and Cynthia Rudin
Hi everyone!
This week at the Applied Statistics Workshop we will be welcoming *Neil
Shephard*, a Professor of Economics and Statistics at Harvard University.
He will be presenting work entitled *Pricing each income contingent student
loan using administrative data. Some statistical challenges*. 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!
-- Anton
Title: Pricing each income contingent student loan using administrative
data. Some statistical challenges
Abstract: Income student loans are used in many countries as the prime way
for students to fund their tuition fees and maintenance. Repayments on the
loans are a fraction of the former student’s income above some threshold.
In England the fraction is 9% and the threshold is around $35,000.
Interest is charged on the loans and any outstanding debt is forgiven after
30 years. The UK Government has offered to issue such loans to any
qualified English student going to a UK university since 1998. How much
are these loans worth to the Government or if the Government sold them?
How does their worth vary with gender, university, subject and time? To
answer these questions we have built a unique database out of
administrative data the UK Government has given us access to. I will
discuss this data, compare it to other sample survey sources and discuss
the statistical challenges we face in pricing. Further, I will argue the
approach I develop here provides a better definition of human capital than
that traditionally used in labor economics.
Hi everyone!
This week at the Applied Statistics Workshop we will be welcoming *Tyler
VanderWeele*, a Professor of Epidemiology at the Harvard School of Public
Health. He will be presenting work entitled *A Unification of Mediation
and Interaction: A 4-Way Decomposition*. Please find the abstract below
and on the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/presentations…>
.
As usual, we will meet in CGIS Knafel Room 354 and lunch will be provided.
See you all there!
-- Anton
Title: A Unification of Mediation and Interaction: A 4-Way Decomposition
Abstract: The overall effect of an exposure on an outcome, in the presence
of a mediator with which the exposure may interact, can be decomposed into
4 components: (1) the effect of the exposure in the absence of the
mediator, (2) the interactive effect when the mediator is left to what it
would be in the absence of exposure, (3) a mediated interaction, and (4) a
pure mediated effect. These 4 components, respectively, correspond to the
portion of the effect that is due to neither mediation nor interaction, to
just interaction (but not mediation), to both mediation and interaction,
and to just mediation (but not interaction). This 4-way decomposition
unites methods that attribute effects to interactions and methods that
assess mediation. Certain combinations of these 4 components correspond to
measures for mediation, whereas other combinations correspond to measures
of interaction previously proposed in the literature. Prior decompositions
in the literature are in essence special cases of this 4-way decomposition.
The 4-way decomposition can be carried out using standard statistical
models, and software is provided to estimate each of the 4 components. The
4-way decomposition provides maximum insight into how much of an effect is
mediated, how much is due to interaction, how much is due to both mediation
and interaction together, and how much is due to neither.
Hi everyone!
This week at the Applied Statistics Workshop we will be welcoming *Sherri
Rose*, a Professor of Biostatistics at Harvard Medical School. She will be
presenting work entitled *Rethinking Plan Payment Risk Adjustment with
Machine Learning*. Please find the abstract below and on the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/presentations…>
.
As usual, we will meet in CGIS Knafel Room 354 and lunch will be provided.
See you all there!
-- Anton
Title: Rethinking Plan Payment Risk Adjustment with Machine Learning
Abstract: Risk adjustment models for plan payment are typically estimated
using classical linear regression models. These models are designed to
predict plan spending, often as a function of age, gender, and diagnostic
conditions. The trajectory of risk adjustment methodology in the federal
government has been largely frozen since the 1970s, failing to incorporate
methodological advances that could yield improved formulas. The use of
novel machine learning techniques may improve estimators for risk
adjustment, including reducing the ability of insurers to "game" the system
with aggressive diagnostic upcoding. This upcoding has been recently
estimated to cost over $11 billion in excess payments in Medicare
Advantage, annually. We present a nonparametric machine learning framework
for risk adjustment in the Truven MarketScan database, and assess whether
use of these procedures improves risk adjustment.
