Social Statistics Seminar Series
Fri, 10/3/2014, 12:00 PM—1:30 PM
4357 Bunche Hall
Front-door Difference-in-Differences Estimators
In this work, we develop front-door difference-in-differences estimators that utilize information from post-treatment variables in addition to information from pre-treatment covariates. Even when the front-door criterion does not hold, these estimators allow the identification of causal effects under assumptions related to standard difference-in-differences assumptions and allow the bounding of causal effects under relaxed assumptions. We illustrate these points with an application to the National JTPA (Job Training Partnership Act) Study and with an application to Florida's early in-person voting program. For the JTPA study, we show that an experimental benchmark can be bracketed with front-door and front-door difference-in-differences estimates. Surprisingly, neither of these estimates use control units. For the Florida program, we find some evidence that early in-person voting had small positive effects on turnout in 2008 and 2012. This provides a counterpoint to recent claims that early voting had a negative effect on turnout in 2008.
Statistics/Social Sciences Faculty Search
Open-ranked tenured and tenure track position, joint in the Department of Statistics and a department or departments in the Division of Social Sciences at the University of California, Los Angeles. We seek a faculty member contributing at the cutting edge to the development of statistical methodology relevant to the social sciences. We welcome candidates whose experience in teaching, research or community service has prepared them to contribute to our commitment to diversity and excellence. Duties include new course development, teaching, and methodological and collaborative research. Ph.D. required by date of anticipated appointment of July 1, 2015. Salary will be commensurate with education and experience. Please refer to job number JPF00372 on all correspondence.
Reviews for the position begin October 13, 2014, and will continue until the position is filled. Interested applicants should apply at UCLA Academic Recruit at:
The University of California is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, age or protected veteran status. For the complete University of California nondiscrimination and affirmative action policy see:
Applicants should submit:
2) Cover letter describing how their qualifications and interests would fit with the position description
3) Statement of Research
4) Statement of Teaching
5) Three letters of recommendation
For more information on the departments in the Division of Social Sciences, see www.sscnet.ucla.edu.
All inquiries may be sent to Professor Mark Handcock at firstname.lastname@example.org.
Professor Emeritus James MacQueen Has Passed Away
It is with much sadness that we announce of the passing of Professor Emeritus James MacQueen. Professor MacQueen passed away on July 15 at the age of 85 after a long illness.
Professor MacQueen joined the faculty of UCLA’s Graduate School of Management in 1962 and was named a full professor in 1970. Prior to joining our faculty, Professor MacQueen had a distinguished career that included academic appointments at the University of Oregon and University of California, Berkeley. He earned his bachelor’s degree in psychology at Reed College in 1952 and his master’s of science and Ph.D., both in psychology, from the University of Oregon in 1954 and 1958 respectively.
Professor MacQueen’s research focused on providing mathematical formulations of human processes. In his first paper, he investigated a large class of optimal stopping problems. This paper contains the first treatment of the house-hunting problem, also called in economics the job search problem or the problem of selling an asset. This has spawned a large area of research. He was also a pioneer in the development of “K-means,” a method of detecting clusters in multivariate data. Professor MacQueen’s other areas of interest include density estimation and Markov processes.
Colleagues have described him as being outgoing, humble, without ego and always willing to give credit to others. Professor MacQueen excelled in providing fresh ideas for problems that needed to be solved. He loved the outdoors, particularly hiking in the Bighorn Mountains of Wyoming. He was an avid player of Kriegspiel, a form of chess without seeing the opponent's moves.
Professor MacQueen is survived by his wife, Ann, their three children, Donald, Kate and Mary, and five grandchildren.
Paper Analyzing Effect of Images in the Media is Reported in the BloombergView
Congratulations to Jungseock Joo, Weixin Li, Francis F. Steen, and Song-Chun Zhu as their work, “Visual Persuasion: Inferring Communicative Intents of Images”, was both presented at IEEE CVPR 2014 and reported in the BloombergView. Their paper analyzed huge numbers of political images in the media in order to determine their likely effects on audiences. It brings together the tools of statistics and machine learning to analysis in political science.
Please see the article in the BloombergView at:
2014 UCLA DataFest is Featured in the Blog fivethirtyeight.com
See this link: http://fivethirtyeight.com/datalab/the-students-most-likely-to-take-our-jobs/
Judea Pearl has been elected to the National Academy of Sciences
Congratulations to Judea Pearl, Professor Emeritus who was elected in April to the National Academy of Sciences. This is one of the highest honors possible for an academic scholar in science, engineering or medicine. It rewards distinguished and continuing achievements in original research.
UCLA Statistics is #8 in QS World University Rankings
UCLA Statistics was ranked # 8 worldwide amongst Statistics & Operational Research programs in 2014. Details are available at:
The department was ranked #14 in 2013. Let's make this a trend!
More Innovation from VCLA
Some members of our VCLA (Vision, Cognition, Learning and Art) research group (from left to right: Yibiao Zhao, Yixin Zhu and Steven Holtzen) are building a cognitive robot, which will not only navigate and recognize objects, but also make sense of the world like a human and answer meaningful questions beyond “what is where”. Answers relating to:
- Functionality: “What is the object used for?”
- Physics: “How likely will the object fall if someone bumps the table?”
- Intentionality: “Why the man left the room without closing the door? Will he come back?”
- Causality: “What did the man do if he come back with a cup of coffee?”
A central goal of computer vision is to create computational systems whose visual recognition and scene understanding accuracy is comparable to, or better than, that of biological vision. Over almost 50 years, the recognition of explicit visual patterns, like face and handwritten digits, has matured to the point of being ubiquitous in modern industrial and consumer products. However, computers still cannot perform many important tasks that are trivial for human vision. Recognizing man-made objects that vary greatly in appearance and shape, but not in function, such as chairs, and anticipating physical dangers, are examples of such tasks. This gap between people and computers exists because computers cannot use rich common sense knowledge about the functional, physical and social mechanics of the world in the ways that people can.
The group aims to close this gap by exploring methods for representing and exploiting common sense world knowledge about function, physics, intentions and causality in ways that can make human-level performance possible in machines. By treating function and physics as principal determinants in how a visual scene is organized by man or by nature, and by describing the relationships between visual entities and ongoing events using a rich notion of causality, computer vision systems can go beyond labeling “what is where” in an image to building a sophisticated understanding of a scene’s three-dimensional organization over time, its physical dynamics, and what actions it affords. In effect, these abilities allow a robot to answer an almost limitless range of questions about an image using a finite and general-purpose model. This may ultimately be instrumental to designing computers that can pass the visual Turing test.
A workshop "Vision meets cognition: functionality, physics, intentionality and causality" is being co-organized by Yibiao Zhao in conjunction with CVPR 2014. This will bring together researchers from different sub-communities within computer vision, computer graphics, robotics and cognitive science, to illuminate and broaden interdisciplinary awareness and collaboration.
Two UCLA Statistics Faculty Win Prestigious "PAMI Helmholtz Test-of-Time Award"
Congratulations to Song-Chun Zhu and Alan Yuille who won the “PAMI Helmholtz Test-of-Time Award”. This was awarded by the IEEE PAMI Technique Committee at the International Conference on Computer Vision (ICCV) held in Sydney in December 2013. They received this honor for the paper entitled, “Region Competition: Unifying Snakes, Region Growing, and Bayes / MDL for Multiband Image Segmentation”, which they co-authored in 1996. The paper developed a new algorithm called “Region Competition” which first linked statistical models of images to partial differential equations (PDEs) for image segmentation. The paper that wins the “PAMI Helmholtz Test-of-Time Award” is one that is frequently cited by other papers in computer vision, judged to have made a powerful impact to the field and must have been published by ICCV more than a decade ago.