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In this lecture, we'll
see how analytics

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can be used to predict the
outcomes of cases in the United

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States Supreme Court.

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This seems like a very
unconventional use

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of analytics, but in 2002 a
group of political science

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and law academics decided to
test if a model can do better

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than a group of
experts at predicting

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the decisions of
the Supreme Court.

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In this case, a very
interpretable analytics method

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was used, called classification
and regression trees.

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The legal system of
the United States

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operates at the
state level and at

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the federal or
country-wide level.

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The federal level is
necessary to deal with cases

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beyond the scope of state law,
like disputes between states,

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and violations of federal laws.

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The federal court is
divided into three levels--

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district courts, circuit
courts, and the Supreme Court.

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Cases start at the
district courts,

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where an initial decision
is made about the case.

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The circuit courts hear appeals
from the district courts,

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and can change the
decision that was made.

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The Supreme Court
is the highest level

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in the American legal system
and makes the final decision

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on cases.

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The Supreme Court
of the United States

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consists of nine
judges, or justices,

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who are appointed
by the President.

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This image shows the nine
Supreme Court justices

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from the time period
1994 through 2005.

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This was the longest
period of time

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with the same set of
justices in over 180 years.

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The people appointed as Supreme
Court justices are usually

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distinguished judges,
professors of law,

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or state or federal atttorneys.

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The Supreme Court of the
United States, or SCOTUS,

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decides on the most difficult
and controversial cases

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in the United States.

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These cases often
involve an interpretation

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of the Constitution, and have
significant social, political,

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and economic consequences.

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There have been many significant
and groundbreaking decisions

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made by the Supreme Court.

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These are a few notable
decisions that were made.

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In 1942, the Supreme
Court decided

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on the Wickard v. Filburn case.

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This case recognized the power
of the federal government

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to regulate economic activity.

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Filburn was a farmer,
who was growing

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wheat for on-farm consumption.

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However, the US had established
limits on wheat production,

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and Filburn was
exceeding those limits.

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So even though the extra
wheat he was producing

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was for his own use and he had
no intention of selling it,

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he was forced to destroy it.

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In 1973, the Supreme Court
decided on the Roe v. Wade

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case, one of the most
well-known cases to this day.

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They decided to legalize
abortion, and by doing this,

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prompted a national debate
that continues today

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about the legality of abortion.

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In 2000, the Supreme
Court actually

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decided the outcome of
the presidential election.

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The race was so close
in the state of Florida,

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that a recount of the
ballots was required.

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But the Florida Secretary
of State certified

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that President Bush was the
winner before the recount

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could be completed.

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The case then went
to the Supreme Court

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where it was ruled that all
ballots needed to be recounted.

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But since this could not be
done before the winner had

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to be declared, President
Bush won the state of Florida,

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and thus, the presidency.

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A very recent case from
2012 dealt with the Patient

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Protection and Affordable
Care Act, commonly

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called ObamaCare, which
requires most Americans

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to have health insurance.

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The Supreme Court
upheld this requirement.

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Since non-profits,
voters, and anybody

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interested in
long-term planning can

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benefit from knowing the
outcomes of the Supreme Court

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cases before they
happen, legal academics

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and political
scientists regularly

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make predictions of
Supreme Court decisions

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from detailed studies of the
cases and individual justices.

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In 2002, Andrew
Martin, a professor

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of political science at
Washington University in St.

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Louis, decided to
instead predict

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decisions using a statistical
model built from data.

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Together with his
colleagues, he decided

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to test the model against
a panel of experts.

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They wanted to see if an
analytical model could

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outperform the
expertise and intuition

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of a large group of experts.

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Martin used a method called
classification and regression

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trees, or CART.

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In this case, the
outcome is binary.

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Will the Supreme Court affirm
the case or reject the case?

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He could have used logistic
regression for this,

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but logistic regression models
are not easily interpretable.

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The model coefficients
in logistic regression

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indicate the importance and
relative effect variables,

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but do not give a
simple explanation

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of how a decision is made.

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In this lecture, we'll
discuss the method of CART,

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and a related method
called random forests.

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We will then see if these
methods can actually

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outperform experts in predicting
the outcome of Supreme Court

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cases.