UAI 2023 - Keynote Speakers
UAI 2023 is pleased to announce the following invited speakers:
Victor Chernozhukov, MIT
Caroline Uhler, MIT
Alexandra Chouldechova, Carnegie Mellon University
Victor Chernozhukov
MITTitle
Long Story Short: Omitted Variable Bias in Causal Machine Learning
Abstract
We derive general, yet simple, sharp bounds on the size of the omitted variable bias for a broad class of causal parameters that can be identified as linear functionals of the conditional expectation function of the outcome. Such functionals encompass many of the traditional targets of investigation in causal inference studies, such as, for example, average of potential outcomes, average treatment effects, average derivatives, and policy effects from shifts in covariate distribution -- all for general, nonparametric causal models. Our construction relies on the Riesz-Frechet representation of the target functional. Specifically, we show how the bound on the bias depends only on the additional variation that the latent variables create both in the outcome and in the Riesz representer for the parameter of interest. Furthermore, we use debiased machine learning to provide flexible and efficient statistical inference on learnable components of the bounds.
Arxiv paper: https://arxiv.org/abs/2112.13398. [video]
Bio
Victor Chernozhukov is the International Ford Professor in the Department of Economics at MIT and Center for Statistics and Data Science of MIT. He received his Ph.D. from Stanford University in 2000, and has worked at MIT since then. He works primarily in econometrics and statistics, with much of recent research focusing on the causal inference using machine learning methods. He is a fellow of The Econometric Society and a recipient of The Alfred P. Sloan Research Fellowship, The Arnold Zellner Award, and The Bessel Award. He was elected to the American Academy of Arts and Sciences in April 2016. In 2019 he was elected a Fellow of the Institute of Mathematical Statistics with the citation “for pathbreaking contributions to high-dimensional inference”.
Caroline Uhler
MITTitle
Causal Representation Learning and Optimal Intervention Design
Abstract
Massive data collection holds the promise of a better understanding of complex phenomena and ultimately, of better decisions. Representation learning has become a key driver of deep learning applications, since it allows learning latent spaces that capture important properties of the data without requiring any supervised annotations. While representation learning has been hugely successful in predictive tasks, it can fail miserably in causal tasks including predicting the effect of an intervention. This calls for a marriage between representation learning and causal inference. An exciting opportunity in this regard stems from the growing availability of interventional data (in medicine, advertisement, education, etc.). However, these datasets are still miniscule compared to the action spaces of interest in these applications (e.g. interventions can take on continuous values like the dose of a drug or can be combinatorial as in combinatorial drug therapies). In this talk, we will present initial ideas towards building a statistical and computational framework for causal representation learning and discuss its applications to optimal intervention design in the context of drug design and single-cell biology.
Slides could be found here. [video]
Bio
Caroline Uhler is a full professor in the Department of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society at MIT. In addition, she is a core institute member at the Broad, where she co-directs the Eric and Wendy Schmidt Center. She holds an MSc in mathematics, a BSc in biology, and an MEd all from the University of Zurich. She obtained her PhD in statistics from UC Berkeley in 2011 and then spent three years as an assistant professor at IST Austria before joining MIT in 2015. She is a SIAM Fellow, a Simons Investigator, a Sloan Research Fellow, and an elected member of the International Statistical Institute. In addition, she received an NIH New Innovator Award, an NSF Career Award, a Sofja Kovalevskaja Award from the Humboldt Foundation, and a START Award from the Austrian Science Foundation. Her research lies at the intersection of machine learning, statistics, and genomics, with a particular focus on causal inference, representation learning, and gene regulation.
Alexandra Chouldechova
Carnegie Mellon UniversityTitle
Algorithms in Unjust Systems
Abstract
Algorithmic bias is often of greatest concern in domains that have been shaped by a long history of discrimination, marginalization, and procedural injustice. In this talk I will discuss what we have learned about the development, deployment, evaluation and impact of predictive risk assessment algorithms in inequitable systems. I will offer examples from the US child welfare, criminal justice, and health care systems. In particular, I will describe the role that problem formulation—specifically, the choice of prediction target—plays as a potential driver of disparities, and will highlight the importance of post-deployment evaluations. Along the way, I will discuss demands for procedural, informational, distributive and interpersonal justice that have emerged from qualitative studies of affected communities and diverse stakeholders. [video]
Bio
Alex Chouldechova is a Principal Researcher in the Fairness, Accountability, Transparency and Ethics (FATE) group at Microsoft Research NYC. She is also the Estella Loomis McCandless Associate Professor of Statistics and Public Policy at Carnegie Mellon University's Heinz College of Information Systems and Public Policy. Her research investigates questions of algorithmic fairness and accountability in pre-trained models and data-driven decision-making systems, with a domain focus on criminal justice and human services. Her work has been supported through funding from organizations including the Hillman Foundation, the MacArthur Foundation, and the NSF Program on Fairness in Artificial Intelligence in Collaboration with Amazon. She is a member of the executive committee for the ACM Conference on Fairness, Accountability and Transparency (FAccT), and previously served as a Program Committee co-Chair for the conference. Dr. Chouldechova was a 2020 Research Fellow with the Partnership on AI, served on the Pittsburgh Task Force on Public Algorithms and the Arnold Ventures Pretrial Research Advisory Board, and was an Amazon Scholar with AWS AI. Dr. Chouldechova received her PhD in Statistics from Stanford University and an H.B.Sc. in Mathematical Statistics from the University of Toronto.
Sponsors
