gov3009-l September 2019

gov3009-l@lists.fas.harvard.edu

1 participants
5 discussions

GOV 3009 Applied Stats Workshop (10/02) - Santiago Olivella

by Georgina Evans

Dear Workshop Community, Our next meeting will be Wednesday October 2, where Santiago Olivella will present work on "Dynamic Stochastic Blockmodel Regression for Social Networks: Application to International Conflicts". Abstract: Many social scientists theorize how various factors predict the dynamic process of network evolution. These theories explain the ways in which nodal and dyadic characteristics play a role in the formation and evolution of relational ties over time. We develop a dynamic model of social networks by combining a hidden Markov model with a mixed-membership stochastic blockmodel that identifies latent groups underlying the network structure. Unlike existing models, we incorporate covariates that predict the dynamic changes in the node membership of latent groups as well as the direct formation of edges between dyads. Our motivating application is the dynamic modeling of international conflicts. While most existing work assumes the decision to engage in militarized conflict is independent across states and static over time, we demonstrate that conflict patterns are driven by states’ evolving membership in geopolitical coalitions. Changes in monadic covariates like democracy shift states between coalitions, generating heterogeneous effects on conflict over time and across states. The proposed methodology, which relies on a variational approximation to a collapsed posterior distribution, is implemented through an open-source software package. Where: CGIS Knafel Building, Room K354 (see this link <https://map.harvard.edu/?bld=04471&level=9> for directions). When: Wednesday, October 2 at 12 noon - 1:30 pm. All are welcome. Lunch will be provided. Best, Georgie

4 years, 6 months

GOV 3009 Applied Stats Workshop (09/25) - Matt Blackwell

by Georgina Evans

Dear Workshop Community, Our next meeting will be Wednesday September 25, where Matt Blackwell will present work on "On Model Dependence in the Estimation of Interactive Effects”. Abstract: Heterogeneous effects are ubiquitous in the social sciences and are often an important component of---and means of assessing---theoretical arguments. A common strategy to assess this heterogeneity is to include a single multiplicative interaction term between the treatment and a hypothesized effect moderator in a regression model. In this paper, we show how inferences about interactions under this approach are highly sensitive to modeling choices about how the effect modifier interacts with other covariates, an issue almost never discussed in practice. We propose an alternative strategy that uses machine learning techniques to allow for more flexible estimation of interactions. We apply this approach to two applications: the effect of direct primary adoption on third-party voting, with heterogeneity by region, and the effects of remittances on political protest as moderated by level of democracy. Where: CGIS Knafel Building, Room K354 (see this link <https://map.harvard.edu/?bld=04471&level=9> for directions). When: Wednesday, September 125th at 12 noon - 1:30 pm. As always, all are welcome! Lunch will be provided. Best, Georgie ** This week, Professor Iain Johnston will spend the first few minutes of the workshop introducing himself as Title IX Liaison for the Government department. Please arrive as close to 12 noon as possible so that we can get start on time. (Note: the Fall schedule for Gov 3009 can be viewed here <https://projects.iq.harvard.edu/applied.stats.workshop-gov3009>)

4 years, 7 months

GOV 3009 Applied Stats Workshop (09/18) - Benjamin Lauderdale

by Georgina Evans

Dear Workshop Community, Our next meeting will be Wednesday September 18, where Benjamin Lauderdale will present work on "Measuring Attitudes towards Public Spending using a Multivariate Tax Summary Experiment”. Abstract: It is difficult to measure public views on tradeoffs between spending priorities because public understanding of existing government spending is limited and the budgetary problem is complicated. We present a new measurement strategy using UK taxpayer summaries as the baseline for a continuous treatment, multivariate choice experiment. The experiment proposes deficit neutral bundles of changes in spending and taxation, allowing us to investigate attitudes towards modifications to the existing budget. We then use a structural choice model to estimate public preferences over 13 spending categories and the taxation level, on average and as a function of respondent attributes. We find that the UK public favours paying more in tax to finance large spending increases across major budget categories; that spending preferences are multidimensional; and that younger people prefer lower levels of taxation and spending than older people. Finally, we report a pre-registered out-of-sample validation of the estimates from the experiment. Where: CGIS South S001 Kin-Chung Lam Room **** Please note the one-off change of location **** When: Wednesday, September 18th at 12 noon - 1:30 pm. All are welcome! Lunch will be provided. Best, Georgie (Note: the Fall schedule for Gov 3009 can be viewed here <https://projects.iq.harvard.edu/applied.stats.workshop-gov3009>)

4 years, 7 months

GOV 3009 Workshop (09/11) - Andrea Rotnitzky

by Georgina Evans

Dear Workshop Community, Our next meeting will be Wednesday September 11, where Andrea Rotnitzky will present work on "Efficient adjustment sets for population average causal effect estimation in graphical models”. Abstract: Covariate adjustment is often used for estimation of population average causal effects (ATE). In recent years graphical rules have been derived for determining, from a causal diagram, all covariate adjustment sets. Restricting attention to causal linear models, a very recent article introduced two graphical criterions: one to compare the asymptotic variance of linear regression estimators that control for certain distinct adjustment sets and a second to identify the optimal adjustment set that provides the smallest asymptotic variance. In this talk, I will show that the same graphical criterions can be used in arbitrary causal diagrams when the goal is to minimize the asymptotic variance of non-parametric estimators of ATE that ignore the causal diagram assumptions. Furthermore, I will provide a graphical criterion to determine the optimal adjustment set among the minimal adjustment sets. In addition, I will provide another graphical criterion for determining when a non-parametric estimator of ATE is as efficient as an efficient estimator that exploits the causal diagram assumptions. Finally, I will show that for estimating the effect of time dependent treatments in the presence of time dependent confounders, there exist diagrams with no optimal adjustment sets. Where: CGIS Knafel Building, Room K354 (see this link <https://map.harvard.edu/?bld=04471&level=9> for directions). When: Wednesday, September 11th at 12 noon - 1:30 pm. All are welcome! Lunch will be provided. Best, Georgie (Note: the Fall schedule for Gov 3009 can be viewed here <https://projects.iq.harvard.edu/applied.stats.workshop-gov3009>)

4 years, 7 months

GOV 3009 First Workshop (09/04) TOMORROW - Anja Sautmann

by Evans, Georgina

Hi Workshop Community, A reminder that our first session will be* tomorrow*, where Anja Sautmann will present co-athored work with Maximilian Kasy on "*Adaptive treatment assignment in experiments for policy choice*". The paper is attached. *Where:* CGIS Knafel Building, Room K354 (see this link <https://map.harvard.edu/?bld=04471&level=9> for directions). *When: *Wednesday, September 4th at 12 noon - 1:30 pm. All are welcome! Lunch will be provided. *Abstract*: *The goal of many experiments is to inform the choice between different policies. However, standard experimental designs are geared toward point estimation and hypothesis testing. We consider the problem of treatment assignment in an experiment with several non-overlapping waves, where the goal is to choose among a set of possible policies (treatments) for large-scale implementation. The optimal experimental design learns from earlier waves and assigns more experimental units to the better-performing treatments in later waves. We propose a computationally tractable approximation of the optimal design that we call “exploration sampling,” where assignment probabilities are an increasing concave function of the posterior probabilities that each treatment is optimal. Theoretical results and calibrated simulations demonstrate improvements in welfare, relative to both non-adaptive designs as well as bandit algorithms. An application to selecting between different recruitment strategies for an agricultural extension service in Odisha, India demonstrates practical feasibility.* Looking forward to seeing you all there, Georgie (Note: the Fall schedule for Gov 3009 can be viewed here <https://projects.iq.harvard.edu/applied.stats.workshop-gov3009>)

4 years, 7 months

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gov3009-l September 2019