gov3009-l September 2020

gov3009-l@lists.fas.harvard.edu

1 participants
5 discussions

Gov 3009 Applied Statistics Workshop (9/30) -- Michael Baiocchi

by Yamauchi, Soichiro

Hi all, Our next virtual meeting will be Wednesday, September 30, where we will hear Michael Baiocchi (Stanford University) presents research on "When black box algorithms are (not) appropriate: a principled prediction-problem ontology." Abstract: In the 1980s a new, extraordinarily productive way of reasoning about algorithms emerged. Though this type of reasoning has come to dominate areas of data science, it has been under-discussed and its impact under-appreciated. For example, it is the primary way we reason about "black box'' algorithms. In this talk we discuss its current use (i.e., as "the common task framework'') and its limitations; we find a large class of prediction-problems are inappropriate for this type of reasoning. Further, we find the common task framework does not provide a foundation for the deployment of an algorithm in a real world situation. Building off of its core features, we identify a class of problems where this new form of reasoning can be used in deployment. We purposefully develop a novel framework so both technical and non-technical people can discuss and identify key features of their prediction problem and whether or not it is suitable for this new kind of reasoning. Zoom link: https://harvard.zoom.us/j/99424949004?pwd=aWtPNFM3ZzFYbWxIMXNoZDlyUElVZz09 (Login required) When: Wednesday, September 30 at 12noon -- 1:30pm. The information and schedule of the seminar can be found on our website <https://projects.iq.harvard.edu/applied.stats.workshop-gov3009/home> and Google calendar https://bit.ly/30QZJ9k. Best, Soichiro Yamauchi -- Soichiro Yamauchi PhD candidate Harvard University URL: https://soichiroy.github.io/

3 years, 7 months

Gov 3009 Applied Statistics Workshop (9/23) -- Reagan Mozer

by Yamauchi, Soichiro

Hi all, Our next virtual meeting will be Wednesday, September 23, where we will hear Reagan Mozer (Bentley University) presents research on "Recent adventures in causal(ish) inference with text as data." Abstract: Text data have a long history in social science and education research. However, these data are notoriously high-dimensional and characterized by many nuances of language that lack plausible statistical models. As a result, analysis of text data typically involves intensive human coding tasks where particular constructs or features of the text are first defined, and then a collection of documents are inspected and coded for the presence or absence of these constructs. While this process may be feasible in studies with smaller sample sizes, the time and resources required to train and employ multiple human coders frequently poses a challenge for large-scale efforts. In this talk, I will consider how to reliably and efficiently extract meaningful constructs from text documents in a manner that preserves human judgment, primarily for the purposes of supporting causal inferences in randomized where some outcomes of interest are features of text generated by the trial’s participants. To illustrate how text data might be leveraged in various inferential settings both in and out of the causal realm, I will present results from three recent studies in education, medicine, and public health. Zoom link: https://harvard.zoom.us/j/99424949004?pwd=aWtPNFM3ZzFYbWxIMXNoZDlyUElVZz09 (Login required) When: Wednesday, September 23 at 12noon -- 1:30pm. The information and schedule of the seminar can be found on our website <https://projects.iq.harvard.edu/applied.stats.workshop-gov3009/home> and Google calendar https://bit.ly/30QZJ9k. Best, Soichiro Yamauchi -- Soichiro Yamauchi PhD candidate Harvard University URL: https://soichiroy.github.io/

3 years, 7 months

Gov 3009 Applied Statistics Workshop (9/16) -- Connor Jerzak

by Yamauchi, Soichiro

Hi all, Our next virtual meeting will be Wednesday, September 16, where we will hear Connor Jerzak (Harvard University; practice job talk) presents research on "Detecting and Characterizing Latent Influence Dynamics in Social Science Data Using Machine Learning." Abstract: Unobserved interactions between people and groups play a fundamental role in domestic and international politics. Yet, despite their importance, the vast complexity of these unobserved interactions has typically frustrated efforts to quantify them, forcing scholars to assume that the units in an analysis are independent or to study a limited range of interactions. Here, I develop a framework and machine learning model for detecting and characterizing unobserved interference dynamics using all available information: outcome, covariate, and independent variable data. Given minimal assumptions, this approach guarantees an analyst-set cap on the rate of false influence detection. It is able to satisfactorily reconstruct the influence structure of a network that was approximately measured by investigators in a school bullying experiment. I apply the method to 12 social science experiments and focus on one of these, a voter turnout intervention in the UK, as a case study. I also discuss the application of this method to the analysis of influence in observational data and in answering questions about individual-level spillovers. Zoom link: https://harvard.zoom.us/j/99424949004?pwd=aWtPNFM3ZzFYbWxIMXNoZDlyUElVZz09 (Login required) When: Wednesday, September 16 at 12noon -- 1:30pm. The information and schedule of the seminar can be found on our website <https://projects.iq.harvard.edu/applied.stats.workshop-gov3009/home> and Google calendar https://bit.ly/30QZJ9k. Best, Soichiro Yamauchi -- Soichiro Yamauchi PhD candidate Harvard University URL: https://soichiroy.github.io/

3 years, 7 months

Gov 3009 Applied Statistics Workshop (9/9) -- Matthew Blackwell

by Yamauchi, Soichiro

Hi all, Our first virtual meeting of the semester will be Wednesday, September 9, where we will hear Matthew Blackwell <https://www.mattblackwell.org/> (Harvard University) presents research on "Noncompliance and instrumental variables for 2^K factorial experiments." *Abstract*: Factorial experiments are widely used to assess the marginal, joint, and interactive effects of multiple concurrent factors. While a robust literature covers the design and analysis of these experiments, there is less work on how to handle treatment noncompliance in this setting. To fill this gap, we introduce a new methodology that uses the potential outcomes framework for analyzing 2^K factorial experiments with noncompliance on any number of factors. This framework builds on and extends the literature on both instrumental variables and factorial experiments in several ways. First, we define novel, complier-specific quantities of interest for this setting and show how to generalize key instrumental variables assumptions. Second, we show how partial compliance across factors gives researchers a choice over different types of compliers to target in estimation. Third, we show how to conduct inference for these new estimands from both the finite-population and superpopulation asymptotic perspectives. Finally, we illustrate these techniques by applying them to two field experiments—one on the effects of cognitive behavioral therapy on crime and the other on the effectiveness of different forms of get-out-the-vote canvassing. New easy-to-use, open-source software implements the methodology. A copy of the paper can be found here <https://www.mattblackwell.org/files/papers/factorial-iv.pdf>. *Zoom link*: https://harvard.zoom.us/j/99424949004?pwd=aWtPNFM3ZzFYbWxIMXNoZDlyUElVZz09 *When*: Wednesday, September 9 at 12noon -- 1:30pm. The information and schedule of the seminar can be found on our website <https://projects.iq.harvard.edu/applied.stats.workshop-gov3009/home> and Google calendar https://bit.ly/30QZJ9k. Best, Soichiro Yamauchi -- Soichiro Yamauchi PhD candidate Harvard University URL: https://soichiroy.github.io/

3 years, 7 months

Applied Statistics Workshop (Gov 3009)

by Yamauchi, Soichiro

Hi all, I am delighted to announce this fall's schedule for the Applied Statistics Workshop (Gov 3009) <https://projects.iq.harvard.edu/applied.stats.workshop-gov3009>. The workshop is a speaker series that hosts speakers both from Harvard and outside each semester to present cutting-edge research on applied statistics. All of this fall's talks are scheduled for *Wednesdays* from *12:00pm to 1:30pm* via Zoom. If you would like to be added to the email list to receive reminders and information about the series, please send your email address to Soichiro Yamauchi (syamauchi(a)g.harvard.edu). We have organized an outstanding schedule for this fall: 9/9: Matthew Blackwell (Harvard University) 9/16: Connor Jerzak (Harvard - Practice Job Talk) 9/23: Reagan Mozer (Bentley University 9/30: Michael Baiocchi (Stanford University) 10/7: Felix Elwert (University of Wisconsin-Madison) 10/14: Luke Miratrix (Harvard University) 10/21: Eric Tchetgen Tchetgen (University of Pennsylvania) 10/28: Fabian Pfeffer (University of Michigan) 11/4: Yiling Chen (Harvard University) 11/11: Cory McCartan (Harvard University) 11/18: Tyler VanderWeele (Harvard University) 12/2: Kristen Hunter (Harvard University) We hope to see many of you at the talks throughout the semester! Best, Soichiro -- Soichiro Yamauchi PhD candidate Harvard University URL: https://soichiroy.github.io/

3 years, 7 months

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gov3009-l September 2020