gov3009-l October 2021

gov3009-l@lists.fas.harvard.edu

1 participants
4 discussions

Gov 3009 Applied Statistics Workshop (10/27) -- No Meeting

by Shin, Sooahn

Dear Applied Statistics Workshop Community, I'm writing this email to inform you that we have *no meeting this week* due to a scheduling change. Xiang Zhou's talk will be moved to Nov 17. Our next meeting will be at 12:10 pm (EST) Wednesday, Nov 3. Schedule of the workshop: https://projects.iq.harvard.edu/applied.stats.workshop-gov3009 Thanks, Sooahn

2 years, 6 months

Gov 3009 Applied Statistics Workshop (10/20) -- Eli Ben-Michael

by Shin, Sooahn

Dear Applied Statistics Workshop Community, Our next meeting of the semester will be at *12:10 pm (EST) Wednesday, October 20*, where Eli Ben-Michael <https://ebenmichael.github.io>(Harvard University) presents "Safe Policy Learning through Extrapolation: Application to Pre-trial Risk Assessment." This is joint work with D. James Greiner (Harvard University), Kosuke Imai (Harvard University), and Zhichao Jiang (University of Massachusetts, Amherst). *Abstract* Algorithmic recommendations and decisions have become ubiquitous in today's society. Many of these and other data-driven policies are based on known, deterministic rules to ensure their transparency and interpretability. This is especially true when such policies are used for public policy decision-making. For example, algorithmic pre-trial risk assessments, which serve as our motivating application, provide relatively simple, deterministic classification scores and recommendations to help judges make release decisions. Unfortunately, existing methods for policy learning are not applicable because they require existing policies to be stochastic rather than deterministic. We develop a robust optimization approach that partially identifies the expected utility of a policy, and then finds an optimal policy by minimizing the worst-case regret. The resulting policy is conservative but has a statistical safety guarantee, allowing the policy-maker to limit the probability of producing a worse outcome than the existing policy. We extend this approach to common and important settings where humans make decisions with the aid of algorithmic recommendations. Lastly, we apply the proposed methodology to a unique field experiment on pre-trial risk assessments. We derive new classification and recommendation rules that retain the transparency and interpretability of the existing risk assessment instrument while potentially leading to better overall outcomes at a lower cost. *Where:* CGIS Knafel Building, Room K354 (See this link <https://map.harvard.edu/?bld=04471&level=9> for directions). *When:* Wednesday, October 20 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, 6 months

Gov 3009 Applied Statistics Workshop (10/13) -- Ruobin Gong

by Shin, Sooahn

Dear Applied Statistics Workshop Community, Our next meeting of the semester will be at *12:10 pm (EST) Wednesday, October 13*, where Ruobin Gong <https://ruobingong.github.io> (Rutgers University) presents "Towards Good Statistical Inference from Differentially Private Data." *Abstract* Differential privacy (DP) brings provability and transparency to statistical disclosure limitation. When data users migrate their analysis onto private data products, there is no guarantee that a statistical model, otherwise suitable for non-private data, can still produce trustworthy conclusions. This talk contemplates two challenges in drawing good statistical inference from private data. When the DP mechanism is transparent, I discuss how approximate computation techniques can be adapted to produce exact inference with respect to the joint specification of the intended model and the DP mechanism. In the presence of mandated invariants which the data curator must observe, I underscore the importance to recognize the associated privacy leakage, and advocate for the congenial design of the DP mechanism as an alternative to optimization-based post-processing, as a way to preserve the statistical intelligibility of the private data product. *Where:* CGIS Knafel Building, Room K354 (See this link <https://map.harvard.edu/?bld=04471&level=9> for directions). *When:* Wednesday, October 13 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, 6 months

Gov 3009 Applied Statistics Workshop (10/6) -- Neil Shephard

by Shin, Sooahn

Dear Applied Statistics Workshop Community, Our next meeting of the semester will be at *12:10 pm (EST) Wednesday, October 6*, where Neil Shephard <https://scholar.harvard.edu/shephard/home> (Harvard University) presents "When do common time series estimands have nonparametric causal meaning." This is joint work with Ashesh Rambachan (Harvard University). Please find the attached paper. *Abstract* The nonparametric potential outcome system provides a foundational framework for giving conditions under which common predictive time series statistical estimands, such as the impulse response function, generalized impulse response function, local projection and local projection instrument variables, have a nonparametric causal interpretation in terms of dynamic causal effects. *Where:* CGIS Knafel Building, Room K354 (See this link <https://map.harvard.edu/?bld=04471&level=9> for directions). *When:* Wednesday, October 6 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, 6 months

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gov3009-l October 2021