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2021

Tyler VanderWeele presents "The Global Flourishing Study - Seeking Analytic Input", at CGIS Knafel Building (K354) - 12:10-1:30 pm, Wednesday, February 9, 2022
The recently launched Global Flourishing Study is a longitudinal research study being carried out in collaboration between scholars at the Human Flourishing Program 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...

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William La Cava presents "Unfairness in AI-based Clinical Decisions: Intersectional Approaches to Measurement and Mitigation", at CGIS Knafel Building (K354) - 12:10-1:30 pm, Wednesday, February 2, 2022
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... Read more about William La Cava presents "Unfairness in AI-based Clinical Decisions: Intersectional Approaches to Measurement and Mitigation"
Tracy Ke presents "Learning Research Areas and Author Research Interests from Bibtex and Citations", at https://harvard.zoom.us/j/97004196610?pwd=eGFydkF5RDRjUlk5RVcyTjV6OStUQT09, Wednesday, January 26, 2022

Given the scientific publications in a field, we are interested in using bibtex and citation data to estimate (a) the primary research areas in this field, (b) the research interests of individual authors (which may evolve with time), and (c) the citation impacts of different research topics in this field. We answer questions (a)-(b) by studying the co-citation networks of authors. We model them by a dynamic mixed-membership model, where each primary area is a “community”,...

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Susan Murphy presents "Assessing Personalization in Digital Health", at CGIS Knafel Building (K354) - 12:10-1:30 pm, Wednesday, December 1, 2021
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...
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Xiang Zhou presents "Higher Education and the Black-White Earnings Gap", at CGIS Knafel Building (K354) - 12:10-1:30 pm, Wednesday, November 17, 2021
Higher education can be a double-edged sword in shaping the black-white earnings gap. It may serve as an equalizer, if black youth benefit more from attending and completing college than white youth. It may also reinforce racial inequality, given that black college-goers are less likely to complete college relative to white students. We employ a novel causal decomposition and a debiased machine learning method to isolate the equalizing and disequalizing effects of college and unveil the sources of these effects. Analyzing data from the NLSY97, we find that among men, a BA degree has a strong... Read more about Xiang Zhou presents "Higher Education and the Black-White Earnings Gap"
Isaiah Andrews presents "Inference on Winners", at CGIS Knafel Building (K354) - 12:10-1:30 pm, Wednesday, November 10, 2021

Many empirical questions concern target parameters selected through optimization. For example, researchers may be interested in the effectiveness of the best policy found in a randomized trial, or the best-performing investment strategy based on historical data. Such settings give rise to a winner’s curse, where conventional estimates are biased and conventional confidence intervals are unreliable. This paper develops optimal confidence intervals and median-unbiased estimators that are valid conditional on the target selected and so overcome this winner’s curse. If one requires validity...

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James Robins presents "Target Trials: Emulating RCTs using Observational Longitudinal Data", at CGIS Knafel Building (K354) - 12:10-1:30 pm, Wednesday, November 3, 2021

Target trials are RCTs one would like to conduct but cannot for ethical, financial, and/or logistical reasons. As a consequence, we must emulate such trials from observational data. A novel aspect of target trial methodology is that, for purposes of data analysis, each subject in the observational study is ‘enrolled’ in all target trials for which the subject is eligible, instead of a single trial. I will compare the strengths and weakness of the target trial...

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Eli Ben-Michael presents "Safe Policy Learning through Extrapolation: Application to Pre-trial Risk Assessment", at CGIS Knafel Building (K354) - 12:10-1:30 pm, Wednesday, October 20, 2021

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...

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Ruobin Gong presents "Towards Good Statistical Inference from Differentially Private Data", at CGIS Knafel Building (K354) - 12:10-1:30 pm, Wednesday, October 13, 2021
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... Read more about Ruobin Gong presents "Towards Good Statistical Inference from Differentially Private Data"
Neil Shephard presents "When do common time series estimands have nonparametric causal meaning", at CGIS Knafel Building (K354) - 12:10-1:30 pm, Wednesday, October 6, 2021

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.

 

This is joint work with Ashesh Rambachan.

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