[Lisa_seminaires] [mila-tous] [Tea Talk] David Blei (Columbia University) Fri March 8 2019 10h30 Mila Auditorium

Yoshua Bengio yoshua.bengio at mila.quebec
Mar 5 Mar 04:59:09 EST 2019


I concur! Do not miss David's talk and come to meet him too.

-- Yoshua

*SVP prendre note de mon nouveau courriel ainsi que de celui de mon
adjointe :*
*Please note my new email address as well as my assistant's one:  *
yoshua.bengio at mila.quebec
julie.mongeau at mila.quebec


Le lun. 4 mars 2019, à 12 h 15, Laurent Charlin <lcharlin at gmail.com> a
écrit :

> Dave has done foundational work and is a leading thinker in the fields of
> probabilistic graphical models and variational inference. He is also an
> excellent speaker. His talk should not be missed!
>
> Best,
> Laurent
>
> On Mon, Mar 4, 2019 at 10:30 AM Rim Assouel <rim.assouel at gmail.com> wrote:
>
>> This week we have * David Blei* from * Columbia University * giving a
>> talk on * The Blessings of Multiple Causes* at * 10h30* at *Mila
>> Auditorium*.
>>
>> Will this talk be streamed <https://mila.bluejeans.com/809027115/webrtc>?
>> Yes
>>
>> David will also be visiting Mila on *Thursday (7th March)*. To that end
>> we organized a discussion group on *Causal Inference* from *2pm *to *3pm* in
>> *room A14*.
>>
>> A bunch of you asked for 1-1 and 2-1 meetings : The schedule is also set
>> during that day, I’ll send the invites by tomorrow :)
>>
>> See you there!
>> The Tea Talk Team
>>
>> *TITLE* The Blessings of Multiple Causes
>>
>> *ABSTRACT*
>> Causal inference from observational data is a vital problem, but it comes
>> with strong assumptions. Most methods require that we observe all
>> confounders, variables that correlate to both the causal variables (the
>> treatment) and the effect of those variables (how well the treatment
>> works). But whether we have observed all confounders is a famously
>> untestable assumption. We describe the deconfounder, a way to do causal
>> inference from observational data with weaker assumptions that the
>> classical methods require. How does the deconfounder work? While
>> traditional causal methods measure the effect of a single cause on an
>> outcome, many modern scientific studies involve multiple causes, different
>> variables whose effects are simultaneously of interest. The deconfounder
>> uses the multiple causes as a signal for unobserved confounders, combining
>> unsupervised machine learning and predictive model checking to perform
>> causal inference. We describe the theoretical requirements for the
>> deconfounder to provide unbiased causal estimates, and show that it
>> requires weaker assumptions than classical causal inference. We analyze the
>> deconfounder's performance in three types of studies: semi-simulated data
>> around smoking and lung cancer, semi-simulated data around genomewide
>> association studies, and a real dataset about actors and movie revenue. The
>> deconfounder provides a checkable approach to estimating close-to-truth
>> causal effects. This is joint work with Yixin Wang. [*]
>> https://arxiv.org/abs/1805.06826
>>
>> *BIO*
>> David Blei is a Professor of Statistics and Computer Science at Columbia
>> University, and a member of the Columbia Data Science Institute. He studies
>> probabilistic machine learning, including its theory, algorithms, and
>> application. David has received several awards for his research, including
>> a Sloan Fellowship (2010), Office of Naval Research Young Investigator
>> Award (2011), Presidential Early Career Award for Scientists and Engineers
>> (2011), Blavatnik Faculty Award (2013), ACM-Infosys Foundation Award
>> (2013), and a Guggenheim fellowship (2017). He is the co-editor-in-chief of
>> the Journal of Machine Learning Research. He is a fellow of the ACM and the
>> IMS. He is a fellow of the ACM and the IMS.
>>
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