9th Causal Inference Workshop at UAI
19th July 2024 - Barcelona
This workshop aims to highlight the latest advancements and explore future directions in causal inference, an area of increasing importance in artificial intelligence. Causal inference enables researchers and practitioners to understand and model cause-effect relationships beyond mere correlations, offering profound implications for data science, machine learning, and AI. Given UAI's focus on the uncertainty aspects of AI, incorporating causal reasoning and inference provides essential insights into building more robust, interpretable, and fair AI decision systems. This workshop will cover a broad spectrum of topics, including but not limited to, causal discovery, causal effect estimation, counterfactual reasoning, and applications of causal inference in machine learning and AI.
Please refer to the call for abstracts for submission details.
Keynote Speakers
Ema Perkovic, Ph.D.
Department of Statistics
University of Washington
Title: TBA
Abstract: TBA
Abstract: TBA
Laura Balzer, Ph.D. MPhil
Division of Biostatistics
School of Public Health
University of California, Berkeley
Title: Targeted Machine Learning with Missing & Dependent Data
Abstract: Despite best intentions, data for causal inference are often subject to complex missingness and dependence. We illustrate with 3 real-world examples from the SEARCH Consortium to (1) assess disease burden and control; (2) evaluate effects among those known to be "at risk", and (3) estimate total effects when measurement is the key mediator. For each, we use hierarchical causal models to define Counterfactual Strata Effects, occurring when the subgroup of interest is impacted by the exposure/intervention. For estimation and inference of these effects, we use TMLE with Super Learner, harnessing recent advances in machine learning and appropriately accounting for uncertainty. We conclude with practical recommendations and areas of ongoing work.
Abstract: Despite best intentions, data for causal inference are often subject to complex missingness and dependence. We illustrate with 3 real-world examples from the SEARCH Consortium to (1) assess disease burden and control; (2) evaluate effects among those known to be "at risk", and (3) estimate total effects when measurement is the key mediator. For each, we use hierarchical causal models to define Counterfactual Strata Effects, occurring when the subgroup of interest is impacted by the exposure/intervention. For estimation and inference of these effects, we use TMLE with Super Learner, harnessing recent advances in machine learning and appropriately accounting for uncertainty. We conclude with practical recommendations and areas of ongoing work.
Panelists
TBA
Organizing Committee
Razieh Nabi, Ph.D.
Biostatistics & Bioinformatics Dept.
Rollins School of Public Health
Emory University
Sara Magliacane, Ph. D.
Amsterdam Machine Learning Lab
Informatics Institute
University of Amsterdam
Karthika Mohan, Ph.D.
School of EECS
College of Engineering
Oregon State University
Caleb Miles, Ph.D.
Dept. of Biostatistics
Mailman School of Public Health
Columbia University
Daniel Malinsky, Ph.D.
Dept. of Biostatistics
Mailman School of Public Health
Columbia University