gov3009-l March 2021

gov3009-l@lists.fas.harvard.edu

1 participants
4 discussions

Gov 3009 Applied Statistics Workshop (3/24) -- Avi Feller

by Yamauchi, Soichiro

Hi all, Our next virtual meeting will be at 12pm (EST) Wednesday, March 24 (tomorrow), where Avi Feller (UC Berkeley) will present "Varying impacts of letters of recommendation on college admissions: Approximate balancing weights for subgroup effects in observational studies." This is joint work with Eli Ben-Michael and Jesse Rothstein. *Abstract*: In a pilot program during the 2016-17 admissions cycle, the University of California, Berkeley invited many applicants for freshman admission to submit letters of recommendation. We use this pilot as the basis for an observational study of the impact of submitting letters of recommendation on subsequent admission, with the goal of estimating how impacts vary across pre-defined subgroups. Understanding this variation is challenging in observational studies, however, because estimated impacts reflect both actual treatment effect variation and differences in covariate balance across groups. To address this, we develop balancing weights that directly optimize for "local balance'' within subgroups while maintaining global covariate balance between treated and control units. We then show that this approach has a dual representation as a form of inverse propensity score weighting with a hierarchical propensity score model. In the UC Berkeley pilot study, our proposed approach yields excellent local and global balance, unlike more traditional weighting methods, which fail to balance covariates within subgroups. We find that the impact of letters of recommendation increases with the predicted probability of admission, with mixed evidence of differences for under-represented minority applicants. *Zoom link*: https://harvard.zoom.us/j/97787602526?pwd=Uzh3bVVVS0F4TEVYQTJlV3BQNjcydz09 *Schedule of the workshop*: https://projects.iq.harvard.edu/applied.stats.workshop-gov3009 Looking forward to seeing you all tomorrow! Best, Soichiro -- Soichiro Yamauchi PhD candidate Harvard University URL: https://soichiroy.github.io/

3 years, 1 month

Gov 3009 Applied Statistics Workshop (3/17) -- Suzanna Linn

by Yamauchi, Soichiro

Hi all, Our next virtual meeting will be at 12pm (EST) Wednesday, March 17, where Suzanna Linn (Penn State University) presents research on "Causal Inference in Dynamic Systems." This is joint work with Clay Webb. *Abstract*: A causal inference revolution has been under way in political methodology for the better part of the last decade. Time series analysts have not been major contributors to this revolution because the tools that have been developed thus far do not fit our data. Existing methods either require analyst to pool observations so that analysts can differentiate treatment and control units, require analysts to identify or develop suitable control series, or require the analyst to exercise control over treatment in a time series experiment. Our goal is to identify the assumptions and conditions required for analysts to make causal inferences with observational time series data when observations cannot be pooled, control series are unavailable, and counterfactuals cannot be forecast. We highlight the critical assumptions for causal identification in structural dynamic systems: partial equilibrium recursivity and conditional exogeneity. We discuss the conditions when these assumptions are plausible, outline tests for conditional exogeneity and structural non-causality, and consider the potential limitations of the proposed framework. When the proposed assumptions are met, standard Granger non-causality tests provide a means for analysts to recover causal estimands. *Zoom link*: https://harvard.zoom.us/j/97787602526?pwd=Uzh3bVVVS0F4TEVYQTJlV3BQNjcydz09 *Schedule of the workshop*: https://projects.iq.harvard.edu/applied.stats.workshop-gov3009 Looking forward to seeing you all on Wednesday! Best, Soichiro -- Soichiro Yamauchi PhD candidate Harvard University URL: https://soichiroy.github.io/

3 years, 1 month

Gov 3009 Applied Statistics Workshop (3/10) -- Adeline Lo

by Yamauchi, Soichiro

Hi all, Our next virtual meeting will be at 12pm (EST) Wednesday, March 10, where Adeline Lo (University of Wisconsin-Madison) presents research on "Mixed-Membership Stochastic Blockmodels for Bipartite Networks: Application to Cosponsorship in the US Senate." *Abstract*: Many networks in political and social research are naturally bipartite — with two distinct types of actors (nodes), and edges connecting exclusively across the actor types. An example of such networks is the one that results from cosponsorship decisions, in which legislators are connected to the bills they support. Typically, researchers who wish to study these networks are forced to "project" or marginalize over one node type, in an effort to form the kinds of unipartite networks that standard models can handle. This can result in aggregation bias and loss of relevant information about the node type that is averaged over. To avoid these issues, we propose an extension of the mixed-membership stochastic blockmodel that operates directly on the bipartite network structure, incorporating both node and dyad-level covariates. We design and implement a fast, scalable stochastic variational algorithm to obtain estimates of latent variables and hyper-parameters, and illustrate our model using data from the 107th and 108th sessions of the U.S. Senate. We find that using our model allows us to uncover interesting groups of bills, explore the effects of individual ideology on the likelihood of bipartisan cosponsorship, and evaluate the norms of reciprocity in the legislative context. *Zoom link*: https://harvard.zoom.us/j/97787602526?pwd=Uzh3bVVVS0F4TEVYQTJlV3BQNjcydz09 *Schedule of the workshop*: https://projects.iq.harvard.edu/applied.stats.workshop-gov3009 Looking forward to seeing you all on Wednesday! Best, Soichiro -- Soichiro Yamauchi PhD candidate Harvard University URL: https://soichiroy.github.io/

3 years, 1 month

Gov 3009 Applied Statistics Workshop (3/3) -- Arthur Yu

by Yamauchi, Soichiro

Hi all, Our next virtual meeting will be at 12pm (EST) Wednesday, March 3, where Arthur Yu (University of Chicago) presents research on "Beyond LATE: Identification of ATEs of Always-Takers and Never-Takers." *Abstract*: In the presence of heterogeneous treatment effects, instrumental variable (IV) estimation point identifies the local average treatment effect, an average treatment effect (ATE) for compliers. This paper provides a set of identification results that extrapolate the LATE to the ATEs of always-takers and never-takers. We first show that the ATEs of always-takers and never-takers can be written as the weighted average of marginal treatment effect (MTE) functions. We then demonstrate that, under additional parametric assumptions on these MTE functions, we can point identify the ATEs of always-takers and never-takers. In the absence of these parametric assumptions we can construct bounds for the ATEs of always-takers and never-takers by linear programming developed in Mogstad et al. (2018), which performs better than the competing partial identification strategies. We illustrate the proposed methodology using a simulation study and an application based on Kern and Hainmueller (2009). We find that exposure to West German television reduces support for communism among never-takers. These never-takers, who would not watch West German TV even if they had improved access, act as-if they anticipate the effect of watching West German TV and thus opt out of exposure. *Zoom link*: https://harvard.zoom.us/j/97787602526?pwd=Uzh3bVVVS0F4TEVYQTJlV3BQNjcydz09 *Schedule of the workshop*: https://projects.iq.harvard.edu/applied.stats.workshop-gov3009 Looking forward to seeing you all on Wednesday! Best, Soichiro -- Soichiro Yamauchi PhD candidate Harvard University URL: https://soichiroy.github.io/

3 years, 1 month

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

gov3009-l March 2021