[Colloquium] REMINDER: 9/19 TTIC Colloquium: Pragya Sur, Harvard University

[Mary Marre]

*When:*        Monday, September 19th at* 11:30 am CT*


*Where:       *Talk will be given *live, in-person* at

                   TTIC, 6045 S. Kenwood Avenue

                   5th Floor, Room 530


*Virtually:*  via Panopto (*livestream*
<https://uchicago.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=3e8d32c2-8607-47c4-8841-af0f01386a94>
)


*Who: *         Pragya Sur, Harvard University


------------------------------

*Title*: A New Perspective on High-Dimensional Causal Inference

*Abstract:* Causal inference from high-dimensional observational studies
poses intriguing challenges. In this context, the augmented inverse
probability weighting estimator is widely used for average treatment effect
estimation. This estimator exhibits fascinating properties, such as double
robustness. However, existing statistical guarantees rely on some form of
sparsity in the underlying model, and may fail to apply in practical
settings when these assumptions are  violated. In this talk, we present a
new central limit theorem for this estimator, that applies in high
dimensions, without sparsity-type assumptions on underlying signals.
Specifically, we work in the proportional asymptotics regime, where the
number of features and samples are both large and comparable. Our work
uncovers novel  high-dimensional phenomena that are strikingly different
from their classical counterparts. To conclude, we discuss opportunities
that arise in our framework, when modern machine-learning-based estimators
are used for learning the  high-dimensional nuisance parameters.  On the
technical front, our work utilizes a novel interplay between three distinct
tools---the theory of deterministic equivalents, approximate message
passing theory, and the leave-one-out approach (alternately known as the
cavity method in statistical physics). This is based on joint work with
Kuanhao Jiang, Rajarshi Mukherjee, and Subhabrata Sen (Harvard).

*Host:* *Nathan Srebro <nati at ttic.edu>*
***********************************************************************************

For more information on the colloquium series or to subscribe to the
mailing list, please see http://www.ttic.edu/colloquium.php

Mary C. Marre
Faculty Administrative Support
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Chicago, IL  60637*
*mmarre at ttic.edu <mmarre at ttic.edu>*


On Sun, Sep 18, 2022 at 5:06 PM Mary Marre <mmarre at ttic.edu> wrote:

> *When:*        Monday, September 19th at* 11:30 am CT*
>
>
> *Where:       *Talk will be given *live, in-person* at
>
>                    TTIC, 6045 S. Kenwood Avenue
>
>                    5th Floor, Room 530
>
>
> *Virtually:*  via Panopto (*livestream*
> <https://uchicago.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=3e8d32c2-8607-47c4-8841-af0f01386a94>
> )
>
>
> *Who: *         Pragya Sur, Harvard University
>
>
> ------------------------------
>
> *Title*: A New Perspective on High-Dimensional Causal Inference
>
> *Abstract:* Causal inference from high-dimensional observational studies
> poses intriguing challenges. In this context, the augmented inverse
> probability weighting estimator is widely used for average treatment effect
> estimation. This estimator exhibits fascinating properties, such as double
> robustness. However, existing statistical guarantees rely on some form of
> sparsity in the underlying model, and may fail to apply in practical
> settings when these assumptions are  violated. In this talk, we present a
> new central limit theorem for this estimator, that applies in high
> dimensions, without sparsity-type assumptions on underlying signals.
> Specifically, we work in the proportional asymptotics regime, where the
> number of features and samples are both large and comparable. Our work
> uncovers novel  high-dimensional phenomena that are strikingly different
> from their classical counterparts. To conclude, we discuss opportunities
> that arise in our framework, when modern machine-learning-based estimators
> are used for learning the  high-dimensional nuisance parameters.  On the
> technical front, our work utilizes a novel interplay between three distinct
> tools---the theory of deterministic equivalents, approximate message
> passing theory, and the leave-one-out approach (alternately known as the
> cavity method in statistical physics). This is based on joint work with
> Kuanhao Jiang, Rajarshi Mukherjee, and Subhabrata Sen (Harvard).
>
> *Host:* *Nathan Srebro <nati at ttic.edu>*
>
> ***********************************************************************************
>
> For more information on the colloquium series or to subscribe to the
> mailing list, please see http://www.ttic.edu/colloquium.php
>
> Mary C. Marre
> Faculty Administrative Support
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Chicago, IL  60637*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
>
> On Tue, Sep 13, 2022 at 3:27 PM Mary Marre <mmarre at ttic.edu> wrote:
>
>> *When:*        Monday, September 19th at* 11:30 am CT*
>>
>>
>> *Where:       *Talk will be given *live, in-person* at
>>
>>                    TTIC, 6045 S. Kenwood Avenue
>>
>>                    5th Floor, Room 530
>>
>>
>> *Virtually:*  via Panopto (*livestream*
>> <https://uchicago.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=3e8d32c2-8607-47c4-8841-af0f01386a94>
>> )
>>
>>
>> *Who: *         Pragya Sur, Harvard University
>>
>>
>> ------------------------------
>>
>> *Title*: A New Perspective on High-Dimensional Causal Inference
>>
>> *Abstract:* Causal inference from high-dimensional observational studies
>> poses intriguing challenges. In this context, the augmented inverse
>> probability weighting estimator is widely used for average treatment effect
>> estimation. This estimator exhibits fascinating properties, such as double
>> robustness. However, existing statistical guarantees rely on some form of
>> sparsity in the underlying model, and may fail to apply in practical
>> settings when these assumptions are  violated. In this talk, we present a
>> new central limit theorem for this estimator, that applies in high
>> dimensions, without sparsity-type assumptions on underlying signals.
>> Specifically, we work in the proportional asymptotics regime, where the
>> number of features and samples are both large and comparable. Our work
>> uncovers novel  high-dimensional phenomena that are strikingly different
>> from their classical counterparts. To conclude, we discuss opportunities
>> that arise in our framework, when modern machine-learning-based estimators
>> are used for learning the  high-dimensional nuisance parameters.  On the
>> technical front, our work utilizes a novel interplay between three distinct
>> tools---the theory of deterministic equivalents, approximate message
>> passing theory, and the leave-one-out approach (alternately known as the
>> cavity method in statistical physics). This is based on joint work with
>> Kuanhao Jiang, Rajarshi Mukherjee, and Subhabrata Sen (Harvard).
>>
>> *Host:* *Nathan Srebro <nati at ttic.edu>*
>>
>> ***********************************************************************************
>>
>> For more information on the colloquium series or to subscribe to the
>> mailing list, please see http://www.ttic.edu/colloquium.php
>>
>>
>>
>> Mary C. Marre
>> Faculty Administrative Support
>> *Toyota Technological Institute*
>> *6045 S. Kenwood Avenue*
>> *Chicago, IL  60637*
>> *mmarre at ttic.edu <mmarre at ttic.edu>*
>>
>
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