gov3009-l November 2018

gov3009-l@lists.fas.harvard.edu

1 participants
7 discussions

Applied Stats Workshop Next Week (Wed. 12/5) - Johann Gagnon-Bartsch

by Connor Jerzak

Dear workshop community, We will convene for the Applied Statistics Workshop (Gov 3009) next week on Wednesday (12/5). The speaker is* Johann Gagnon-Bartsch *(U Michigan Stats) who will be presenting his work "The Duality of Negative Controls and Replicates". *Where:* CGIS Knafel Building, Room K354 (see this link <https://map.harvard.edu/?bld=04471&level=9> for directions). *When: *Wednesday, December 5th at 12 noon - 1:30 pm. *Abstract:* Negative controls can be used to adjust for unobserved confounders in an observational study. A negative control is a variable that is known a priori to be (1) unaffected by treatment, and (2) affected by the unobserved confounders. Any observed variation in a negative control may be attributed to the confounders, but not to treatment. Thus, negative controls can be used to partially identify the unobserved confounders. A similar situation arises when a single observational unit is observed multiple times, under varying conditions of the confounders. The multiple observations are referred to as replicates. Any observed variation between the replicates may be attributed purely to the confounders. Thus, like negative controls, replicates can be used to partially identify the confounding variables. Importantly, in a high-dimensional setting, the partial identification provided by negative controls and the partial identification provided by the replicates are in some sense dual to one another. More to the point, these two partial identifications are not redundant, but rather complimentary, and therefore negative controls and replicates can be used together to more fully identify and control for unobserved confounders. *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>.

5 years, 4 months

Applied Stats Workshop Tomorrow (Wed. 11/28) - Tracy Ke

by Connor Jerzak

*FINAL REMINDER --- Applied Statistics Workshop TOMORROW (11/28) at 12 noon* *Lunch provided --- All are welcome * ------------------------------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------------------------------ Dear workshop community, We will convene for the Applied Statistics Workshop (Gov 3009) tomorrow on Wednesday (11/28). The speaker is* Tracy Ke *(Harvard Stats) who will be presenting her work "Statistical Analysis of Large Social Networks". <http://www.mattblackwell.org/files/papers/telescope_matching.pdf> *Where:* CGIS Knafel Building, Room K354 (see this link <https://map.harvard.edu/?bld=04471&level=9> for directions). *When: *Wednesday, November 28th --- 12 noon - 1:30 pm. *Abstract:* Severe degree heterogeneity is a universal phenomenon in large social networks. However, the degree parameters are largely nuisance to our major interest, and their effects can be carefully removed with proper statistical strategies. In the first part of the talk, I will take the mixed-membership estimation as an example and present several useful ideas for dealing with degree heterogeneity. We assume the network has K perceivable communities. Each node is associated with a K-dimensional “membership” vector whose entries describe the nodes’ “weights” on different communities. The goal is to estimate these membership vectors. We adopt a degree-corrected mixed-membership model and propose a spectral method that is conceptually simple, computationally fast, and rate-optimal. *¶* In the second part, I will showcase a dataset we have collected about coauthor/citation networks of statisticians. The data set consists of the meta information (e.g., authors, abstracts, citation counts, etc.) of about 70,000 papers in 36 representative journals in statistics and related fields, from 1984-2015. The dataset provides a fertile ground for methodological comparisons and for scientific discoveries. We report some Exploratory Data Analysis (EDA) results, such as productivity, journal-journal citation exchanges, and citation patterns of individual papers. *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>.

5 years, 5 months

Applied Stats Workshop Next Week (Wed. 11/21) - NO MEETING DUE TO HOLIDAY

by Connor Jerzak

Dear workshop community, There will be no Applied Statistics Workshop next week due to the Thanksgiving holiday. We will convene on November 28th to hear from Tracy Ke of the Harvard University Department of Statistics. Best wishes, 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>.

5 years, 5 months

Applied Stats Workshop Tomorrow (Wed. 11/14) - Matthew Blackwell

by Connor Jerzak

*FINAL REMINDER --- Applied Statistics Workshop TOMORROW (11/14) at 12 noon* *Lunch provided --- All are welcome * ------------------------------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------------------------------ Dear workshop community, We will convene for the Applied Statistics Workshop (Gov 3009) tomorrow on Wednesday (11/14). The speaker is* Matthew Blackwell *(Harvard Gov) who will be presenting his work "Telescope Matching: A Flexible Approach to Estimating Direct Effects" (paper link <http://www.mattblackwell.org/files/papers/telescope_matching.pdf>). *Where:* CGIS Knafel Building, Room K354 (see this link <https://map.harvard.edu/?bld=04471&level=9> for directions). *When: *Wednesday, November 14th at 12 noon - 1:30 pm. *Abstract:* Estimating the direct effect of a treatment fixing the value of a consequence of that treatment is becoming a common part of social science research. In many cases, however, these effects are difficult to estimate standard methods since they can induce post-treatment bias. More complicated methods like marginal structural models or structural nested mean models can recover direct effects in these situations but require parametric models for the outcome or the post-treatment covariates. In this paper, we propose an alternative approach, which we call telescope matching, to estimating direct effects. The method combines matching and regression to impute missing counterfactual outcomes in a flexible manner. Using simulation and empirical studies, we show how this approach weakens model dependence for researchers estimating direct treatment effects. *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>.

5 years, 5 months

Applied Stats Workshop Next Week (Wed. 11/14) - Matthew Blackwell

by Connor Jerzak

Dear workshop community, We will convene for the Applied Statistics Workshop (Gov 3009) next week on Wednesday (11/14). The speaker is* Matthew Blackwell *(Harvard Gov) who will be presenting his work "Telescope Matching: A Flexible Approach to Estimating Direct Effects" (paper link <http://www.mattblackwell.org/files/papers/telescope_matching.pdf>). *Where:* CGIS Knafel Building, Room K354 (see this link <https://map.harvard.edu/?bld=04471&level=9> for directions). *When: *Wednesday, November 14th at 12 noon - 1:30 pm. *Abstract:* Estimating the direct effect of a treatment fixing the value of a consequence of that treatment is becoming a common part of social science research. In many cases, however, these effects are difficult to estimate standard methods since they can induce post-treatment bias. More complicated methods like marginal structural models or structural nested mean models can recover direct effects in these situations but require parametric models for the outcome or the post-treatment covariates. In this paper, we propose an alternative approach, which we call telescope matching, to estimating direct effects. The method combines matching and regression to impute missing counterfactual outcomes in a flexible manner. Using simulation and empirical studies, we show how this approach weakens model dependence for researchers estimating direct treatment effects. *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>.

5 years, 5 months

Applied Stats Workshop Tomorrow (Wed. 11/7) - Zhichao Jiang

by Connor Jerzak

*FINAL REMINDER --- Applied Statistics Workshop TOMORROW (11/7) at 12 noon* *Lunch provided --- All are welcome * ------------------------------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------------------------------ Dear workshop community, We will convene for the Applied Statistics Workshop (Gov 3009) tomorrow on Wednesday (11/7). The speaker is* Zhichao Jiang *(Harvard Gov) who will be presenting his work "Causal Inference with Interference and Noncompliance in Two-Stage Randomized Experiments". *Where:* CGIS Knafel Building, Room K354 (see this link <https://map.harvard.edu/?bld=04471&level=9> for directions). *When: *Wednesday, November 7th at 12 noon - 1:30 pm. *Abstract:* In many social science experiments, subjects often interact with each other and as a result one unit's treatment influences the outcome of another unit. Over the last decade, a significant progress has been made towards causal inference in the presence of such interference between units. However, much of the literature has assumed perfect compliance with treatment assignment. In this paper, we establish the nonparametric identification of the complier average direct and spillover effects in two-stage randomized experiments with interference and noncompliance. In particular, we consider the spillover effect of the treatment assignment on the treatment receipt as well as the spillover effect of the treatment receipt on the outcome. We propose consistent estimators and derive their randomization-based variances under the stratified interference assumption. We also prove the exact relationship between the proposed randomization-based estimators and the popular two-stage least squares estimators. Our methodology is motivated by and applied to the randomized evaluation of the India's National Health Insurance Program (RSBY). The proposed methods are implemented via an open-source software package. *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>.

5 years, 5 months

Applied Stats Workshop Next Week (Wed. 11/7) - Zhichao Jiang

by Connor Jerzak

Dear workshop community, We will convene for the Applied Statistics Workshop (Gov 3009) next week on Wednesday (11/7). The speaker is* Zhichao Jiang *(Harvard Gov) who will be presenting his work "Causal Inference with Interference and Noncompliance in Two-Stage Randomized Experiments". *Where:* CGIS Knafel Building, Room K354 (see this link <https://map.harvard.edu/?bld=04471&level=9> for directions). *When: *Wednesday, November 7th at 12 noon - 1:30 pm. *Abstract:* In many social science experiments, subjects often interact with each other and as a result one unit's treatment influences the outcome of another unit. Over the last decade, a significant progress has been made towards causal inference in the presence of such interference between units. However, much of the literature has assumed perfect compliance with treatment assignment. In this paper, we establish the nonparametric identification of the complier average direct and spillover effects in two-stage randomized experiments with interference and noncompliance. In particular, we consider the spillover effect of the treatment assignment on the treatment receipt as well as the spillover effect of the treatment receipt on the outcome. We propose consistent estimators and derive their randomization-based variances under the stratified interference assumption. We also prove the exact relationship between the proposed randomization-based estimators and the popular two-stage least squares estimators. Our methodology is motivated by and applied to the randomized evaluation of the India's National Health Insurance Program (RSBY). The proposed methods are implemented via an open-source software package. *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>.

5 years, 5 months

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gov3009-l November 2018