gov3009-l

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717 discussions

Gov 3009 Applied Statistics Workshop (3/30) -- Hannah Druckenmiller [Zoom]

by Shin, Sooahn

Dear Applied Statistics Workshop Community, Our next meeting of the semester will be at *12:10 pm (EST) Wednesday, March 30*, where Hannah Druckenmiller <https://hannahdruckenmiller.com> (Resources for the Future) presents "Accounting for Unobservable Heterogeneity in Cross Section Using Spatial First Differences." Please note that this meeting will be *entirely on Zoom <https://harvard.zoom.us/j/97004196610?pwd=eGFydkF5RDRjUlk5RVcyTjV6OStUQT09>* . *Abstract* We develop a simple cross-sectional research design to identify causal effects that is robust to unobservable heterogeneity. When many observational units are dense in physical space, it may be sufficient to regress the “spatial first differences” (SFD) of the outcome on the treatment and omit all covariates. This approach is conceptually similar to first differencing approaches in time-series or panel models, except the index for time is replaced with an index for locations in space. The SFD design identifies plausibly causal effects, so long as local changes in the treatment and unobservable confounders are not systematically correlated between immediately adjacent neighbors. We demonstrate the SFD approach by recovering new cross-sectional estimates for the effects of time-invariant geographic factors, soil and climate, on long-run average crop productivities across US counties — relationships that are notoriously confounded by unobservables but crucial for guiding economic decisions, such as land management and climate policy. *Zoom link*: https://harvard.zoom.us/j/97004196610?pwd=eGFydkF5RDRjUlk5RVcyTjV6OStUQT09 *When:* Wednesday, March 30 at 12:10 - 1:30 pm. *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 (3/23) -- José R. Zubizarreta

by Shin, Sooahn

Dear Applied Statistics Workshop Community, Our next meeting of the semester will be at *12:10 pm (EST) Wednesday, March 23*, where José R. Zubizarreta <http://jrzubizarreta.com/> (Harvard University) presents "Bridging Matching, Regression, and Weighting as Mathematical Programs for Causal Inference." *Abstract* A fundamental principle in the design of observational studies is to approximate the randomized experiment that would have been conducted under controlled circumstances. Across the health and social sciences, statistical methods for covariate adjustment are used in pursuit of this principle. Typical methods are matching, regression, and weighting. In this talk, we will examine the connections between these methods through their underlying mathematical programs. We will study their strengths and weaknesses in terms of study design, computational tractability, and statistical efficiency. Overall, we will discuss the role of mathematical optimization for the design and analysis of studies of causal effects. *Where:* CGIS Knafel Building, Room K354 (See this link <https://map.harvard.edu/?bld=04471&level=9> for directions). *When:* Wednesday, March 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 (3/9) -- Iavor Bojinov

by Shin, Sooahn

Dear Applied Statistics Workshop Community, Our next meeting of the semester will be at *12:10 pm (EST) Wednesday, March 9*, where Iavor Bojinov <https://www.ibojinov.com> (Harvard University) presents "Design and Analysis of Switchback Experiments." *Abstract* Switchback experiments, where a firm sequentially exposes an experimental unit to random treatments, are among the most prevalent designs used in the technology sector, with applications ranging from ride-hailing platforms to online marketplaces. Although practitioners have widely adopted this technique, the derivation of the optimal design has been elusive, hindering practitioners from drawing valid causal conclusions with enough statistical power. We address this limitation by deriving the optimal design of switchback experiments under a range of different assumptions on the order of the carryover effect --- the length of time a treatment persists in impacting the outcome. We cast the optimal experimental design problem as a minimax discrete optimization problem, identify the worst-case adversarial strategy, establish structural results, and solve the reduced problem via a continuous relaxation. For switchback experiments conducted under the optimal design, we provide two approaches for performing inference. The first provides exact randomization based $p$-values, and the second uses a new finite population central limit theorem to conduct conservative hypothesis tests and build confidence intervals. We further provide theoretical results when the order of the carryover effect is misspecified and provide a data-driven procedure to identify the order of the carryover effect. We conduct extensive simulations to study the numerical performance and empirical properties of our results, and conclude with practical suggestions. *Where:* CGIS Knafel Building, Room K354 (See this link <https://map.harvard.edu/?bld=04471&level=9> for directions). *When:* Wednesday, March 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

2 years, 2 months

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, 2 months

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, 3 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

2 years, 3 months

Gov 3009 Applied Statistics Workshop (2/2) -- William La Cava

by Shin, Sooahn

Dear Applied Statistics Workshop Community, Our next meeting of the semester will be at *12:10 pm (EST) Wednesday, February 2*, where William La Cava <http://williamlacava.com> (Harvard University) presents "Unfairness in AI-based Clinical Decisions: Intersectional Approaches to Measurement and Mitigation." *Abstract* Clinical decision support systems increasingly rely on machine learning (ML) models to recommend courses of action. As a result, these systems have the potential to exacerbate inequities in healthcare allocation and disadvantage historically and contemporarily marginalized groups. To address this risk, fair ML algorithms have been proposed that minimize differences in model performance among patient groups. I will discuss some of these methods and the challenges to implementing them in practice. Two major challenges are to measure and mitigate these differences when we consider grouping patients by intersections of demographic variables such as age, race, ethnicity, sex, and socio-economic status. *Where:* CGIS Knafel Building, Room K354 (See this link <https://map.harvard.edu/?bld=04471&level=9> for directions). *When:* Wednesday, February 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, 3 months

Gov 3009 Applied Statistics Workshop (1/26) -- Tracy Ke [Zoom]

by Shin, Sooahn

Dear Applied Statistics Workshop Community, Welcome back to another semester of the Applied Statistics Workshop. Our first meeting of the Spring semester will be at *12:10 pm (EST) Wednesday, January 26, via Zoom* (link <https://harvard.zoom.us/j/97004196610?pwd=eGFydkF5RDRjUlk5RVcyTjV6OStUQT09>). Please note that the first meeting will be *entirely on Zoom*, and from the second meeting (Feb 2), we will continue to meet every Wednesday from 12:10 - 1:30 pm throughout the semester in CGIS Knafel, Room K354. The schedule for the semester is below: Jan 26 [Zoom only]: Tracy Ke (Harvard University) Feb 2: William La Cava (Harvard University) Feb 9: Tyler VanderWeele (Harvard University) Feb 16: Edward McFowland III (Harvard University) Feb 23: Soroush Saghafian (Harvard University) Mar 2: Sharad Goel (Harvard University) Mar 9: Iavor Bojinov (Harvard University) Mar 16: No meeting (Spring recess) Mar 23: José R. Zubizarreta (Harvard University) Mar 30 [Zoom only]: Hannah Druckenmiller (Resources for the Future) Apr 6: Adeline Lo (University of Wisconsin-Madison; Visiting faculty affiliate at Harvard University) Apr 13: Deirdre Bloome (Harvard University) Apr 20: Soubhik Barari (Harvard University) Apr 27 [Zoom only]: Tasha Fairfield (London School of Economics and Political Science) The title and abstract of the first presentation will be shared soon. I look forward to seeing you all virtually on Wednesday! Best, Sooahn

2 years, 3 months

Gov 3009 Applied Statistics Workshop (12/1) -- Susan Murphy

by Shin, Sooahn

Dear Applied Statistics Workshop Community, Our next meeting of the semester will be at 12:10 pm (EST) Wednesday, December 1, where Susan Murphy<http://people.seas.harvard.edu/~samurphy/> (Harvard University) presents "Assessing Personalization in Digital Health." Abstract Reinforcement Learning provides an attractive suite of online learning methods for personalizing interventions in a Digital Health. However after an reinforcement learning algorithm has been run in a clinical study, how do we assess whether personalization occurred? We might find users for whom it appears that the algorithm has indeed learned in which contexts the user is more responsive to a particular intervention. But could this have happened completely by chance? We discuss some first approaches to addressing these questions. Where: CGIS Knafel Building, Room K354 (See this link<https://map.harvard.edu/?bld=04471&level=9> for directions). When: Wednesday, December 1 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, 5 months

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