gov3009-l March 2020

gov3009-l@lists.fas.harvard.edu

1 participants
4 discussions

Gov 3009 Applied Statistics Workshop (04/01) -- Soichiro Yamauchi

by Evans, Georgina

Hi all, Our next virtual meeting will be Wednesday April 1, where we will hear Soichiro Yamauchi present research on “*Difference-in-Differences for Ordinal Outcomes: Application to the Effect of Mass Shootings on Attitudes towards Gun Control*”. *Abstract*: The difference-in-differences (DID) design is widely used in observational studies to estimate the causal effect of a treatment when repeated observations over time are available. Yet, almost all existing methods assume linearity in the potential outcome (parallel trends assumption) and target the additive effect. In social science research, however, many outcomes of interest are measured on an ordinal scale. This makes the linearity assumption inappropriate because the difference between two ordinal potential outcomes is not well defined. In this paper, I propose a method to draw causal inferences for ordinal outcomes under the DID design. Unlike existing methods, the proposed method utilizes the latent variable framework to handle the non-numeric nature of the outcome, enabling identification and estimation of causal effects based on the assumption on the quantile of the latent continuous variable. The paper also proposes an equivalence-based test to assess the plausibility of the key identification assumption when additional pre-treatment periods are available. The proposed method is applied to a study estimating the causal effect of mass shootings on the public’s support for gun control. I find that the effect is concentrated on left-leaning respondents who experienced the shooting for the first time in more than a decade. A copy of the paper can be found here <https://soichiroy.github.io/files/papers/ordinal_did.pdf> . *Zoom link*: https://harvard.zoom.us/j/987462892 *When*: Wednesday, April 1 at 12noon - 1:30pm. Best, Georgie

4 years

Gov 3009 Applied Statistics Workshop (03/25) -- Weihua An

by Evans, Georgina

Hi all, I hope everyone is safe and healthy! Applied Stats will continue for the remainder of the semester via zoom. Our first virtual meeting will be *Wednesday March 25*, where we will hear Weihua An present research on “*Causal Inference with Networked Treatment Diffusion*”. *Abstract:* Causal inference under treatment interference (i.e., one unit’s potential outcomes depend on other units’ treatment) is a challenging but important problem. Past studies usually make strong assumptions on the structure of treatment interference. In this study, I will highlight the importance of collecting data on actual treatment diffusion in order to more accurately measure treatment interference. Furthermore, I will show that with accurate measures of treatment interference, one can identify and estimate a series of causal effects that are previously unavailable, including the direct treatment effect, the treatment interference effect, and the treatment effect on interference. Last, I will use exponential random graph models to model treatment diffusion networks in order to reveal covariates and network processes that significantly correlate with treatment diffusion. I will illustrate the ideas and methods through a case study of a smoking prevention intervention conducted in six middle schools in China. The findings provide an empirical basis to evaluate previous assumptions on the structure of treatment interference, are informative for imputing treatment diffusion when it is unavailable, and help improve designs of future interventions that aim to optimize treatment diffusion. *Zoom link: * https://harvard.zoom.us/j/987462892 *When: *Wednesday, March 25 at 12noon - 1:30pm. I hope many of you can join! Lunch will sadly not be provided. Best, Georgie

4 years, 1 month

Gov 3009 Applied Statistics Workshop (03/11) - Reagan Mozer

by Evans, Georgina

Hi all, Our next meeting will be *Wednesday March 11*, where Reagan Mozer will present research on* "**New approaches for scaling-up human coding efforts in randomized trials with text-based outcomes*". *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 for the purposes of drawing causal inferences, with an emphasis on the context of experimental studies where some outcomes of interest are features of text generated by the trial’s participants. In particular, I will describe an approach that combines machine learning and survey sampling methods to streamline the process of hand-coding in a way that is automatically verified and validated. To illustrate the proposed methods, I will present results from a pilot analysis of a randomized trial that used student-generated essays to evaluate the impact of an educational intervention on students’ writing abilities. *Where:* CGIS Knafel Building, Room K354 (see this link <https://map.harvard.edu/?bld=04471&level=9> for directions). *When: *Wednesday, March 11 at 12noon - 1:30pm. All are welcome and lunch will be provided. Best, Georgie

4 years, 1 month

Gov 3009 Applied Statistics Workshop (03/04) - Professor James Robins

by Evans, Georgina

Hi all, Our next meeting will be *Wednesday March 4*, where Professor James Robins will present research on* “**Causal Mediation Analysis and The Philosophy of Causation**”.* *Abstract:* I introduce an interventionist view of mediation analysis that, unlike standard non-interventionist mediation analysis of Pearl and Vanderweele, (i) does not assume that the mediator has well defined causal effects and/or counterfactuals , (ii) preserves the dictum “ no causation without manipulation” (iii) renders questions of mediation scientific, because eventually testable, (iv) replaces the current complex definitions of path specific effects as nested counterfactuals with easily understood definitions in terms of concrete experimental interventions, (v) facilitates communication with subject matter experts (vi) makes concrete the concept of a “recanting witness” and makes testable the hypothesis that a particular variable is a ‘recanting witness’, (vii) when identified from data, the identifying formulae under an interventionist view and the non-interventionist view are identical; however the effect being identified differs. This talk is based on a joint paper with Ilya Shpitser and Thomas Richardson that generalizes an earlier paper by myself and Thomas Richardson. *Where:* CGIS Knafel Building, Room K354 (see this link <https://map.harvard.edu/?bld=04471&level=9> for directions). *When: *Wednesday, March 4 at 12noon - 1:30pm. All are welcome and lunch will be provided. Best, Georgie

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gov3009-l March 2020