gov3009-l February 2022

gov3009-l@lists.fas.harvard.edu

1 participants
4 discussions

Gov 3009 Applied Statistics Workshop (3/2) -- Sharad Goel

by Shin, Sooahn

Dear Applied Statistics Workshop Community, Our next meeting of the semester will be at *12:10 pm (EST) Wednesday, March 2*, where Sharad Goel <https://5harad.com> (Harvard University) presents "Designing Equitable Algorithms for Criminal Justice and Beyond." *Abstract* Machine learning algorithms are now used to automate routine tasks and to guide high-stakes decisions, but, if not carefully designed, they can exacerbate inequities. I’ll start by describing an evaluation of automated speech recognition (ASR) tools, which power popular virtual assistants, facilitate automated closed captioning, and enable digital dictation platforms for health care. We find that five state-of-the-art ASR systems -- developed by Amazon, Apple, Google, IBM, and Microsoft -- exhibited substantial racial disparities, making twice as many errors for Black speakers compared to white speakers, a gap we trace back to a lack of diversity in the audio data used to train the models. I'll then describe recent attempts to mathematically formalize fairness. I'll argue that some of the most popular definitions, when used as a design principle, can, perversely, harm the very groups they were created to protect. I'll conclude by describing a general, consequentialist paradigm for designing equitable algorithms that aims to mitigate the limitations of the dominant approaches to building fair machine learning systems. *Where:* CGIS Knafel Building, Room K354 (See this link <https://map.harvard.edu/?bld=04471&level=9> for directions). *When:* Wednesday, March 2 at 12:10 - 1:30 pm. (Bagged lunches available for pick-up at CGIS K354 *11:30 - 11:45 am*, for the participants who responded to our previous survey. The CGIS cafe on the first floor has been designated as an eating area, and participants may also use outdoor spaces for lunch. Please be present at K354 by 12:10 pm for the presentations.) *Zoom link*: https://harvard.zoom.us/j/97004196610?pwd=eGFydkF5RDRjUlk5RVcyTjV6OStUQT09 (For the participants who cannot join the session physically.) *Schedule of the workshop*: https://projects.iq.harvard.edu/applied.stats.workshop-gov3009 Looking forward to seeing you all on Wednesday! Best, Sooahn

2 years, 1 month

Gov 3009 Applied Statistics Workshop (2/23) -- Soroush Saghafian

by Shin, Sooahn

Dear Applied Statistics Workshop Community, Our next meeting of the semester will be at *12:10 pm (EST) Wednesday, February 23*, where Soroush Saghafian <https://scholar.harvard.edu/saghafian/home> (Harvard University) presents "Ambiguous Dynamic Treatment Regimes: A Reinforcement Learning Approach." The full paper is available here <https://scholar.harvard.edu/saghafian/publications/ambiguous-dynamic-treatm…> . *Abstract* A main research goal in various studies is to use an observational data set and provide a new set of counterfactual guidelines that can yield causal improvements. Dynamic Treatment Regimes (DTRs) are widely studied to formalize this process and enable researchers to find guidelines that are both personalized and dynamic. However, available methods in finding optimal DTRs often rely on assumptions that are violated in real-world applications (e.g., medical decision-making or public policy), especially when (a) the existence of unobserved confounders cannot be ignored, and (b) the unobserved confounders are time-varying (e.g., affected by previous actions). When such assumptions are violated, one often faces ambiguity regarding the underlying causal model that is needed to be assumed to obtain an optimal DTR. This ambiguity is inevitable, since the dynamics of unobserved confounders and their causal impact on the observed part of the data cannot be understood from the observed data. Motivated by a case study of finding superior treatment regimes for patients who underwent transplantation in our partner hospital and faced a medical condition known as New Onset Diabetes After Transplantation (NODAT), we propose a new framework termed Ambiguous Dynamic Treatment Regimes (ADTRs), in which the casual impact of treatment regimes is evaluated based on a “cloud” of potential causal models. We then connect ADTRs to Ambiguous Partially Observable Mark Decision Processes (APOMDPs) proposed by Saghafian (2018), and consider unobserved confounders as latent variables but with ambiguous dynamics and causal effects on observed variables. Using this connection, we develop two Reinforcement Learning methods termed Direct Augmented V-Learning (DAV-Learning) and Safe Augmented V-Learning (SAV-Learning), which enable using the observed data to efficiently learn an optimal treatment regime. We establish theoretical results for these learning methods, including (weak) consistency and asymptotic normality. We further evaluate the performance of these learning methods both in our case study (using clinical data) and in simulation experiments (using synthetic data). We find promising results for our proposed approaches, showing that they perform well even compared to an imaginary oracle who knows both the true causal model (of the data generating process) and the optimal regime under that model. *Where:* CGIS Knafel Building, Room K354 (See this link <https://map.harvard.edu/?bld=04471&level=9> for directions). *When:* Wednesday, February 23 at 12:10 - 1:30 pm. (Bagged lunches available for pick-up at CGIS K354 *11:30 - 11:45 am*, for the participants who responded to our previous survey. The CGIS cafe on the first floor has been designated as an eating area, and participants may also use outdoor spaces for lunch. Please be present at K354 by 12:10 pm for the presentations.) *Zoom link*: https://harvard.zoom.us/j/97004196610?pwd=eGFydkF5RDRjUlk5RVcyTjV6OStUQT09 (For the participants who cannot join the session physically.) *Schedule of the workshop*: https://projects.iq.harvard.edu/applied.stats.workshop-gov3009 Looking forward to seeing you all on Wednesday! Best, Sooahn

2 years, 2 months

Gov 3009 Applied Statistics Workshop (2/16) -- Edward McFowland III

by Shin, Sooahn

Dear Applied Statistics Workshop Community, Our next meeting of the semester will be at *12:10 pm (EST) Wednesday, February 16*, where Edward McFowland III <https://www.hbs.edu/faculty/Pages/profile.aspx?facId=772797> (Harvard University) presents "Anomalous Pattern Detection: A Novel Lens for Scientific Inquiry." *Abstract* There has been a growing interest in the use of machine learning methods for causal inference, which often involves adjusting or reappropriating predictive models, with causality in mind. As an alternative, anomaly detection methods offer a unique lens through which to conduct causal inference, as the presence of a causal effect results in treatment group units that appear anomalous in comparison to the control group. Moreover, anomalous pattern detection intentionally localizes the presence of treatment effects, which has tremendous value when the ultimate goal involves hypothesis generation, understanding causal mechanisms, or targeting subpopulations. As motivation, we will consider the identification of subpopulations in randomized experiments with extremely significant effects, and will consider other quasi-experimental settings as time permits. *Where:* CGIS Knafel Building, Room K354 (See this link <https://map.harvard.edu/?bld=04471&level=9> for directions). *When:* Wednesday, February 16 at 12:10 - 1:30 pm. (Bagged lunches available for pick-up at CGIS K354 *11:30 - 11:45 am*, for the participants who responded to our previous survey. The CGIS cafe on the first floor has been designated as an eating area, and participants may also use outdoor spaces for lunch. Please be present at K354 by 12:10 pm for the presentations.) *Zoom link*: https://harvard.zoom.us/j/97004196610?pwd=eGFydkF5RDRjUlk5RVcyTjV6OStUQT09 (For the participants who cannot join the session physically.) *Schedule of the workshop*: https://projects.iq.harvard.edu/applied.stats.workshop-gov3009 Looking forward to seeing you all on Wednesday! Best, Sooahn

2 years, 2 months

Gov 3009 Applied Statistics Workshop (2/9) -- Tyler VanderWeele

by Shin, Sooahn

Dear Applied Statistics Workshop Community, Our next meeting of the semester will be at *12:10 pm (EST) Wednesday, February 9*, where Tyler VanderWeele <https://www.hsph.harvard.edu/tyler-vanderweele/> (Harvard University) presents "The Global Flourishing Study - Seeking Analytic Input." *Abstract* The recently launched Global Flourishing Study <https://hfh.fas.harvard.edu/files/pik/files/globalflourishingstudy_report.p…> is a longitudinal research study being carried out in collaboration between scholars at the Human Flourishing Program <https://hfh.fas.harvard.edu/> at Harvard's Institute for Quantitative Social Science, Baylor’s Institute for Studies of Religion, Gallup, and the Center for Open Science. The study will involve data collection for approximately 240,000 participants, from 22 geographically and culturally diverse countries, with nationally representative samples within each country, and with annual data collection on the same panel of individuals for five waves of data. The survey includes a rich set of questions on well-being along with demographic, social, economic, political, religious, personality, childhood, community, health and character-based questions. The data will constitute an open-access resource available to scholars throughout the world. However, in addition to what are hoped to be diverse and wide-ranging uses of the data, the primary research team intends to carry out a series of coordinated parallel pre-registered analyses. The talk will give an overview of the Global Flourishing Study itself and the flourishing framework that motivated it, along with current analysis plans for the coordinated pre-registered studies, with the aim of receiving critique, suggestions, and feedback from the Applied Statistics Workshop participants. Open questions will be put forward concerning appropriate meta-analytic summaries, confounder control with a large number of highly correlated indicators, and challenges of missing data and attrition, all while respecting complex survey weights, the limitations of existing software, and the desire to allow the utilization of multiple software packages given the size and diversity of the primary research team. *Where:* CGIS Knafel Building, Room K354 (See this link <https://map.harvard.edu/?bld=04471&level=9> for directions). *When:* Wednesday, February 9 at 12:10 - 1:30 pm. (Bagged lunches available for pick-up at CGIS K354 *11:30 - 11:45 am*, for the participants who responded to our previous survey. The CGIS cafe on the first floor has been designated as an eating area, and participants may also use outdoor spaces for lunch. Please be present at K354 by 12:10 pm for the presentations.) *Zoom link*: https://harvard.zoom.us/j/97004196610?pwd=eGFydkF5RDRjUlk5RVcyTjV6OStUQT09 (For the participants who cannot join the session physically.) *Schedule of the workshop*: https://projects.iq.harvard.edu/applied.stats.workshop-gov3009 Looking forward to seeing you all on Wednesday! Best, Sooahn

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gov3009-l February 2022