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

Laurent Charlin lcharlin at gmail.com
Lun 4 Mar 12:14:52 EST 2019


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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