The Conrady Applied Science introductory presentation at the ACM Data Mining in San Jose on October 15, 2011.

1. 10/15/11
It’s
a
great
pleasure
and
privilege
to
be
here
today
at
the
ACM
Data
Mining
Camp
in
San
Jose.
We
are
delighted
to
once
again
par�cipate
in
this
event
as
a
Gold
Sponsor.
Many
thanks
for
invi�ng
us
back
this
year!
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2. 10/15/11
As
the
name
of
our
product
implies,
we
are
all
about
promo�ng
Bayesian
networks
as
a
framework
and
BayesiaLab
as
a
so�ware
tool.
Of
all
the
possible
mo�va�ons
for
using
Bayesian
network,
such
as
knowledge
discovery
in
high-­‐dimensional
domains,
I
want
to
focus
on
another,
o�en
neglected
topic,
namely
causality.
But
before
we
get
to
that
par�cular
point,
please
allow
me
to
recap
some
of
the
dominant
headlines
in
our
industry.
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3. 10/15/11
I
don’t
need
to
tell
you
that
big
data
is
probably
the
single
most
frequently
used
buzzword
in
our
industry.
As
data
miners
we
are
(and
should
be)
delighted
that
we
can
draw
upon
this
richness
of
informa�on.
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4. 10/15/11
And,
our
analy�cs
tools
are
becoming
more
and
more
powerful
and
sophis�cated.
The
group
assembled
here
today
probably
knows
be�er
than
anybody
else
what
tremendous
progress
has
been
made
in
the
field
of
analy�cs
and
data
mining.
4

5. 10/15/11
Honestly,
all
of
us
feel
pre�y
good
about
our
algorithms
and
advanced
sta�s�cal
methods.
Our
services
as
data
scien�sts
are
certainly
in
great
demand,
not
only
here
in
Silicon
Valley
(just
think
about
how
much
recrui�ng
is
going
on
here
today!).
Between
big
data
and
supercompu�ng,
we
feel
indeed
very
powerful
with
our
knowledge.
5

6. 10/15/11
However,
does
that
mean
we
are
omniscient?
Do
we
really
understand
the
subjects
we
are
studying
with
our
fancy
tools?
Can
we
truly
generate
a
deep
understanding
of
our
problem
domains?
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7. 10/15/11
I
don’t
want
to
take
my
five
minutes
on
the
podium
here
to
go
into
a
metaphysical
direc�on,
but
rather
reference
Judea
Pearl’s
explana�on
of
“deep
understanding.”
He
says:
“Deep
understanding
means
knowing,
not
merely
how
things
behaved
yesterday,
but
also
how
things
will
behave
under
new
hypothe�cal
circumstances.”
Thus
he
makes
the
clear
dis�nc�on
between
observa�onal
and
causal
inference.
Deep
understanding
requires
knowledge
of
the
causal
mechanism.
This
will
not
necessarily
surprise
us,
as
we
o�en
hear
the
warning
“Correla�on
does
not
imply
causa�on.”
We
will
all
nod
in
agreement
and
carry
on.
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8. 10/15/11
The
problem
is
that
we
are
quite
good
at
observa�onal
inference,
with
robust
sta�s�cal
tools,
while
our
methods
for
causal
inference
are
o�en
rather
tenuous.
This
metaphor
of
a
steel
chain
and
a
string
highlights
the
weakness
in
our
understanding
and,
as
a
result,
our
reasoning.
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9. 10/15/11
As
a
consequence
of
this
imbalance
of
capabili�es,
we
o�en
do
not
address
causality
directly,
but
rather
take
the
“don’t
ask,
don’t
tell”
approach.
I’m
exaggera�ng
to
make
my
point,
but
analysts
o�en
choose
non-­‐commi�al
phrases
in
expressing
their
findings
and
then
let
their
audience
make
up
their
own
causal
conclusions
-­‐
at
their
own
risk.
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10. 10/15/11
There
is
indeed
no
easy
automated
method
for
discovering
causal
rela�onships
and
genera�ng
causal
inference,
but
there
is
a
framework
that
facilitates
causal
representa�on
in
very
formal
way:
Bayesian
networks.
They
allow
us
to
precisely
encode
non-­‐causal
and
causal
dependencies
between
the
variables
of
interest
and
then
leverage
this
knowledge
to
the
fullest
extent
possible.
10

11. 10/15/11
Beyond
evangelizing
about
Bayesian
networks,
we
are
here
to
promote
our
BayesiaLab
so�ware
as
an
integrated
pla�orm
for
learning,
analyzing
and
simula�ng
Bayesian
networks
and,
most
importantly,
carrying
out
causal
inference.
11

12. 10/15/11
Although
we
are
rela�vely
small
in
terms
of
our
company
size,
we
can
confidently
point
to
a
long
list
of
highly-­‐respected
companies
and
academic
ins�tu�ons,
many
of
which
are
Fortune
500
companies.
They
have
come
to
recognize
Bayesian
networks
and
BayesiaLab
as
powerful
tools
for
exploring
and
researching
all
kinds
of
problem
domains.
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13. 10/15/11
We
invite
you
to
visit
us
today
at
our
exhibi�on
booth
here
on
the
eBay
campus
in
order
to
learn
more
about
the
power
of
Bayesian
networks.
Thank
you
for
your
a�en�on
and
have
a
great
day
here
at
the
Data
Mining
Camp!
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