[ds-undergrads] Data Science talk by Prof. Soojin Park, Friday June 3rd, 12-1pm, MRB Seminar Room]

[tsotras at cs.ucr.edu]

Dear DS majors:
here is information about the seminar tomorrow; all are welcome! (please
register with the link below if you plan to attend)
This is the last seminar for this quarter; good luck with your finals and
enjoy the summer!

V. Tsotras


---------------------------- Original Message ----------------------------
Subject: [UCR_DataScience] Data Science talk by Prof. Soojin Park, Friday
June 3rd, 12-1pm, MRB Seminar Room
From:    "Vassilis Tsotras" <tsotras at cs.ucr.edu>
Date:    Tue, May 31, 2022 1:48 pm
To:      datascience at lists.ucr.edu
--------------------------------------------------------------------------

The next Data Science talk will be on Friday June 3rd, 2022, from
12:00-1:00pm at the MRB Seminar Room (1st floor).

**** Pizza and refreshments will be provided ****

To keep track of the number of attendees, please *register* at:
https://www.eventbrite.com/e/data-science-talk-tickets-350752198267

The talk will be given by Prof. Soojin Park, School of Education, UCR

Title: Estimation and Sensitivity Analysis for Causal Decomposition:
Assessing Robustness Toward Omitted Variable Bias.

Abstract:

A key objective of decomposition analysis is to identify risks or
resources (‘mediators’) that contribute to disparities between groups of
individuals defined by social characteristics such as race, ethnicity,
gender, class, and sexual orientations. In decomposition analysis, a
scholarly interest often centers on estimating how much the disparity
(e.g., health disparities between Black women and White men) would be
reduced/remain if we set the mediator (e.g., education) distribution of
one social group equal to another. However, causally identifying disparity
reduction and remaining depends on the no omitted mediator-outcome
confounding assumption, which is not empirically testable. In this talk,
we discuss a flexible way to 1) estimate disparity reduction and remaining
and 2) assess the robustness of the estimates to the possible violation of
no omitted mediator-outcome confounding. We apply the proposed methods to
an empirical example, examining the contribution of education in reducing
health disparities across race-gender groups. Our proposed methods are
available as open-source software (‘causal.decomp’ R package)

Joint work with Suyeon Kang, Chioun Lee, and Shujie Ma


------------------------------------
Sponsored by the UCR Data Science Center, the purpose of the Data Science
talks is to foster collaborations between "core" Data Science faculty
(from CSE/ECE/Stat Departments) and faculty/visitors from other sciences
that face Data Science problems in their research. These informal
gatherings are open to interested faculty and graduate students. Each
meeting will start with a talk describing research problems and then a
discussion will follow for questions, open problems, ideas for possible
collaborations etc.

A full list of previous seminars appears at: http://datascience.ucr.edu/news

Please forward this email to other colleagues or graduate students in your
lab that may be interested.

Moreover, if you are interested in giving a Data Science related talk,
please contact me.

Sincerely,
Vassilis Tsotras
Professor, CSE Department
Director, Data Science Major

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