Data Sciences Speaker Series: Tamara Broderick

Data Sciences Institute

Data Science Speaker Series 2022-23

The DSSS is intended as a collaborative marquee series, whose focus is data science and its intersection with other fields. Together, we seek to advance knowledge in the field of data science by featuring world-class speakers.

Professor Tamara Broderick from the Department of Electrical Engineering and Computer Science, MIT joins the Data Sciences Speaker Series (DSSS). The DSSS is intended as a collaborative marquee series, whose focus is data science and its intersection with other fields. Together, we seek to advance knowledge in the field of data science by featuring world-class speakers from academic, healthcare, industry, finance, technology, and other sectors and industries. In doing so, we hope to facilitate the exchange of ideas, information, and knowledge among researchers, practitioners, and other professionals, and to enhance educational opportunities for students and trainees.This talk is co-sponsored by the Data Sciences Institute and the Master of Science in Applied Computing at the University of Toronto.<ul><li>Date: Monday, September 19, 2022</li><li>Time: 4 pm – 5 pm EDT</li><li>Format: Virtual </li></ul>Talk Title: An Automatic Finite-Sample Robustness Metric: Can Dropping a Little Data Change Conclusions?Abstract: One hopes that data analyses will be used to make beneficial decisions regarding people&#x27;s health, finances, and well-being. But the data fed to an analysis may systematically differ from the data where these decisions are ultimately applied. For instance, suppose we analyze data in one country and conclude that microcredit is effective at alleviating poverty; based on this analysis, we decide to distribute microcredit in other locations and in future years. We might then ask: can we trust our conclusion to apply under new conditions? If we found that a very small percentage of the original data was instrumental in determining the original conclusion, we might expect the conclusion to be unstable under new conditions. So we propose a method to assess the sensitivity of data analyses to the removal of a very small fraction of the data set. Analyzing all possible data subsets of a certain size is computationally prohibitive, so we provide an approximation. We call our resulting method the Approximate Maximum Influence Perturbation. Our approximation is automatically computable, theoretically supported, and works for common estimators --- including (but not limited to) OLS, IV, GMM, MLE, MAP, and variational Bayes. We show that any non-robustness our metric finds is conclusive. Empirics demonstrate that while some applications are robust, in others the sign of a treatment effect can be changed by dropping less than 0.1% of the data
--- even in simple models and even when standard errors are small.

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Data Sciences Speaker Series: Tamara Broderick

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