Dear workshop community,
We will convene for the Harvard University Applied Statistics Workshop (Gov
3009) next week on Wednesday (4/3).
The speaker is* Kosuke Imai *(Harvard) who will be presenting his work,
"Automated Coding of Political Campaign Advertisement Videos: An Empirical
Validation Study".
*Where:* CGIS Knafel Building, Room K354 (see this link
<https://map.harvard.edu/?bld=04471&level=9> for directions).
*When: *Wednesday, April 3rd at 12 noon - 1:30 pm.
*Abstract: *
Television advertisements play an essential role in modern political
campaigns with several billion dollars spent in the 2018 general election.
For more than two decades, political scientists have studied TV ads by
analyzing the hand-coded data from the Wisconsin Advertising Project (WAP)
and its successor, the Wesleyan Media Project (WMP). Unfortunately,
manually coding hundreds of variables, such as issue mentions, opponent
appearance, and negativity, for many videos is a laborious and expensive
process. We propose to automatically code political campaign advertisement
videos. Applying state-of-the-art machine learning methods, we
automatically extract various audio and image features from each video
file. We show that our machine coding is at least as accurate as human
coding for many variables of the WAP/WMP data sets. Since many candidates
make their advertisement videos available on the Internet, automated coding
can dramatically improve the efficiency and scope of campaign advertisement
research. Joint work with June Hwang and Alex Tarr.
*All are welcome! Lunch is provided! *
Best,
Connor Jerzak
Applied Statistics Workshop -- Graduate Student Coordinator
An anonymous feedback form for the workshop can be found here at this link
<https://docs.google.com/forms/d/e/1FAIpQLScp4lPVBtp4Akf6K6ggmfcTUSIUHEJX89-…>.
Workshop listserv sign-up at this link
<https://lists.fas.harvard.edu/mailman/listinfo/gov3009-l>.
Dear workshop community,
We will convene for the Harvard University Applied Statistics Workshop (Gov
3009) TOMORROW on Wednesday (3/27).
The speaker is* Michael Levin *(Tufts University) who will be presenting
his work, "Decision-making without brains: how biological systems process
information".
*Where:* CGIS Knafel Building, Room K354 (see this link
<https://map.harvard.edu/?bld=04471&level=9> for directions).
*When: *Wednesday, March 27th at 12 noon - 1:30 pm.
*Abstract: *
The cognitive powers of the brain evolved from much more ancient processes
in which cells, tissues, and even molecular networks had to make decisions
to optimize their function in a challenging world. In this talk, I will
discuss the field of primitive cognition, focusing on a number of examples
in which non-neural biological systems process information and make
decisions. These include a) cells during embryogenesis, regeneration, and
cancer, b) unicellular organisms such as slime molds, and c) synthetic
organisms. I will also discuss non-neural bioelectricity - an ancient
precursor to brain function, which enables collectives of cells to
cooperate toward large-scale goals. Implications of our work to crack the
bioelectric code extend from regenerative medicine to synthetic
bioengineering and even artificial intelligence.
*All are welcome! Lunch is provided! *
Best,
Connor Jerzak
Applied Statistics Workshop -- Graduate Student Coordinator
An anonymous feedback form for the workshop can be found here at this link
<https://docs.google.com/forms/d/e/1FAIpQLScp4lPVBtp4Akf6K6ggmfcTUSIUHEJX89-…>.
Workshop listserv sign-up at this link
<https://lists.fas.harvard.edu/mailman/listinfo/gov3009-l>.
Dear workshop community,
We will convene for the Harvard University Applied Statistics Workshop (Gov
3009) next week on Wednesday (3/27).
The speaker is* Michael Levin *(Tufts University) who will be presenting
his work, "Decision-making without brains: how biological systems process
information".
*Where:* CGIS Knafel Building, Room K354 (see this link
<https://map.harvard.edu/?bld=04471&level=9> for directions).
*When: *Wednesday, March 27th at 12 noon - 1:30 pm.
*Abstract: *
The cognitive powers of the brain evolved from much more ancient processes
in which cells, tissues, and even molecular networks had to make decisions
to optimize their function in a challenging world. In this talk, I will
discuss the field of primitive cognition, focusing on a number of examples
in which non-neural biological systems process information and make
decisions. These include a) cells during embryogenesis, regeneration, and
cancer, b) unicellular organisms such as slime molds, and c) synthetic
organisms. I will also discuss non-neural bioelectricity - an ancient
precursor to brain function, which enables collectives of cells to
cooperate toward large-scale goals. Implications of our work to crack the
bioelectric code extend from regenerative medicine to synthetic
bioengineering and even artificial intelligence.
*All are welcome! Lunch is provided! *
Best,
Connor Jerzak
Applied Statistics Workshop -- Graduate Student Coordinator
An anonymous feedback form for the workshop can be found here at this link
<https://docs.google.com/forms/d/e/1FAIpQLScp4lPVBtp4Akf6K6ggmfcTUSIUHEJX89-…>.
Workshop listserv sign-up at this link
<https://lists.fas.harvard.edu/mailman/listinfo/gov3009-l>.
Dear workshop community,
We will convene for the Harvard University Applied Statistics Workshop (Gov
3009) TOMORROW on Wednesday (3/13).
The speaker is* Mathias Sinning *(Australian National University) who will
be presenting his work, "Estimating Quantiles of the Distribution of
Treatment Effects".
*Where:* CGIS Knafel Building, Room K354 (see this link
<https://map.harvard.edu/?bld=04471&level=9> for directions).
*When: *Wednesday, March 13th at 12 noon - 1:30 pm.
*Abstract: *
This paper proposes an approach to estimate quantiles of the distribution
of treatment effects under the identifying assumption that treatment
assignment is based on observed characteristics. We use a matching approach
to derive the distribution of treatment effects from differences in
outcomes between matched treatment and control units. Our parameters of
interest may be interpreted as generalized versions of the quantile
treatment effect (QTE) and the quantile treatment effect on the treated
(QTT), which can be identified without imposing a rank preservation
assumption. We prove consistency and asymptotic normality of our estimators
and show that replacing the variances with estimated variances does not
affect the asymptotic distributions. We apply the approach to study the
effects of a job training program on earnings. We find that while the
average treatment effect on the treated is positive, about 40% of
individuals in the treatment group have significantly lower earnings than
comparable individuals in the control group.
*All are welcome! Lunch is provided! *
Best,
Connor Jerzak
Applied Statistics Workshop -- Graduate Student Coordinator
An anonymous feedback form for the workshop can be found here at this link
<https://docs.google.com/forms/d/e/1FAIpQLScp4lPVBtp4Akf6K6ggmfcTUSIUHEJX89-…>.
Workshop listserv sign-up at this link
<https://lists.fas.harvard.edu/mailman/listinfo/gov3009-l>.
Dear workshop community,
We will convene for the Harvard University Applied Statistics Workshop (Gov
3009) next week on Wednesday (3/13).
The speaker is* Mathias Sinning *(Australian National University) who will
be presenting his work, "Estimating Quantiles of the Distribution of
Treatment Effects".
*Where:* CGIS Knafel Building, Room K354 (see this link
<https://map.harvard.edu/?bld=04471&level=9> for directions).
*When: *Wednesday, March 13th at 12 noon - 1:30 pm.
*Abstract: *
This paper proposes an approach to estimate quantiles of the distribution
of treatment effects under the identifying assumption that treatment
assignment is based on observed characteristics. We use a matching
approach to derive the distribution of treatment effects from differences
in outcomes between matched treatment and control units. Our parameters of
interest may be interpreted as generalized versions of the quantile
treatment effect (QTE) and the quantile treatment effect on the treated
(QTT), which can be identified without imposing a rank preservation
assumption. We prove consistency and asymptotic normality of our estimators
and show that replacing the variances with estimated variances does not
affect the asymptotic distributions. We apply the approach to study the
effects of a job training program on earnings. We find that while the
average treatment effect on the treated is positive, about 40% of
individuals in the treatment group have significantly lower earnings than
comparable individuals in the control group.
*All are welcome! Lunch is provided! *
Best,
Connor Jerzak
Applied Statistics Workshop -- Graduate Student Coordinator
An anonymous feedback form for the workshop can be found here at this link
<https://docs.google.com/forms/d/e/1FAIpQLScp4lPVBtp4Akf6K6ggmfcTUSIUHEJX89-…>.
Workshop listserv sign-up at this link
<https://lists.fas.harvard.edu/mailman/listinfo/gov3009-l>.
Dear workshop community,
We will convene for the Applied Statistics Workshop (Gov 3009) TOMORROW on
Wednesday (3/06).
The speaker is* Ingmar Weber *(Research Director, Social Computing Group
at Qatar Computing Research Institute) who will be presenting his work,
"Tapping Into Public Advertising Data to Monitor Migration, Gender Gaps,
Poverty and, Maybe, Censorship".
*Where:* CGIS Knafel Building, Room K354 (see this link
<https://map.harvard.edu/?bld=04471&level=9> for directions).
*When: *Wednesday, March 6th at 12 noon - 1:30 pm.
*Abstract: *
Facebook, LinkedIn and other social networks provide advertisers with
“audience estimates” on how many of their users match certain targeting
criteria. These estimates are usually used for budget planning and include
targeting criteria such as (i) countries a user has lived in, (ii) their
gender, and (iii) the type of mobile device they use. In this talk I report
on how we work with UN agencies and other partners to use this type of
information to monitoring international migration, track digital gender
gaps and map poverty. I’ll also discuss some observations around sudden
fluctuations in the number of daily active users which seem to be related
to temporary country-level blocks of these platforms.
*All are welcome! Lunch is provided! *
Best,
Connor Jerzak
Applied Statistics Workshop -- Graduate Student Coordinator
An anonymous feedback form for the workshop can be found here at this link
<https://docs.google.com/forms/d/e/1FAIpQLScp4lPVBtp4Akf6K6ggmfcTUSIUHEJX89-…>.
Workshop listserv sign-up at this link
<https://lists.fas.harvard.edu/mailman/listinfo/gov3009-l>.
Dear workshop community,
We will convene for the Applied Statistics Workshop (Gov 3009) next week on
Wednesday (3/06).
The speaker is* Ingmar Weber *(Research Director, Social Computing Group
at Qatar Computing Research Institute) who will be presenting his work,
"Tapping Into Public Advertising Data to Monitor Migration, Gender Gaps,
Poverty and, Maybe, Censorship".
*Where:* CGIS Knafel Building, Room K354 (see this link
<https://map.harvard.edu/?bld=04471&level=9> for directions).
*When: *Wednesday, March 6th at 12 noon - 1:30 pm.
*Abstract: *
Facebook, LinkedIn and other social networks provide advertisers with
“audience estimates” on how many of their users match certain targeting
criteria. These estimates are usually used for budget planning and include
targeting criteria such as (i) countries a user has lived in, (ii) their
gender, and (iii) the type of mobile device they use. In this talk I report
on how we work with UN agencies and other partners to use this type of
information to monitoring international migration, track digital gender
gaps and map poverty. I’ll also discuss some observations around sudden
fluctuations in the number of daily active users which seem to be related
to temporary country-level blocks of these platforms.
*All are welcome! Lunch is provided! *
Best,
Connor Jerzak
Applied Statistics Workshop -- Graduate Student Coordinator
An anonymous feedback form for the workshop can be found here at this link
<https://docs.google.com/forms/d/e/1FAIpQLScp4lPVBtp4Akf6K6ggmfcTUSIUHEJX89-…>.
Workshop listserv sign-up at this link
<https://lists.fas.harvard.edu/mailman/listinfo/gov3009-l>.
