Caroline Uhler: Causal inference in the light of drug repurposing for COVID-19
"Causal inference in the light of drug repurposing for COVID-19"
Caroline Uhler, MIT
Abstract: Massive data collection holds the promise of a better understanding of complex phenomena and ultimately, of better decisions. An exciting opportunity in this regard stems from the growing availability of perturbation / intervention data (drugs, knockouts, overexpression, etc.) in biology. In order to obtain mechanistic insights from such data, a major challenge is the development of a framework that integrates observational and interventional data and allows predicting the effect of yet unseen interventions or transporting the effect of interventions observed in one context to another. I will present a framework for causal structure discovery based on such data and characterize the causal relationships that are identifiable in this setting. We end by demonstrating how these ideas can be applied for drug repurposing in the current SARS-CoV-2 crisis.
July 7, 2020
Видео Caroline Uhler: Causal inference in the light of drug repurposing for COVID-19 канала Online Causal Inference Seminar
Caroline Uhler, MIT
Abstract: Massive data collection holds the promise of a better understanding of complex phenomena and ultimately, of better decisions. An exciting opportunity in this regard stems from the growing availability of perturbation / intervention data (drugs, knockouts, overexpression, etc.) in biology. In order to obtain mechanistic insights from such data, a major challenge is the development of a framework that integrates observational and interventional data and allows predicting the effect of yet unseen interventions or transporting the effect of interventions observed in one context to another. I will present a framework for causal structure discovery based on such data and characterize the causal relationships that are identifiable in this setting. We end by demonstrating how these ideas can be applied for drug repurposing in the current SARS-CoV-2 crisis.
July 7, 2020
Видео Caroline Uhler: Causal inference in the light of drug repurposing for COVID-19 канала Online Causal Inference Seminar
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
Tim Morrison: Optimality in multivariate tie-breaker designs
Sam Pimentel: Optimal tradeoffs in matched designs
Hyunseung Kang: Transfer Learning Between U.S. Presidential Elections
Elizabeth Ogburn: Social network dependence, unmeasured confounding, and the replication crisis
Anish Agarwal: On Causal Inference with Temporal and Spatial Spillovers in Panel Data
Sara Magliacane: Domain adaptation by using causal inference
Interview with Philip Dawid
Nicola Gnecco: Causal Discovery in Heavy-Tailed Models
Carlos Cinelli: Transparent and Robust Causal Inference in the Social and Health Sciences
Qingyuan Zhao: Selection Bias in 2020
Donald Green: Using Placebo-Controlled Designs to Detect Edutainment Effects and Spillovers
AmirEmad Ghassami: Combining Experimental and Observational Data for Long-Term Causal Effects
Kun Zhang: Methodological advances in causal representation learning
Stefan Wager: Treatment Effects in Market Equilibrium
Fan Li: Causal Mediation Analysis for Sparse and Irregular Longitudinal Data
Thijs van Ommen: Graphical Representations for Algebraic Constraints of Linear Structural Models
Alex Luedtke: Adversarial Monte Carlo Meta-Learning of Conditional Average Treatment Effects
Bin Yu: Predictability, stability, and causality with case study of genetic drivers of heart disease
David Blei: The Deconfounder: What is it? What is its theory? Is it useful?
Marloes Maathuis: Combining causal structure learning and covariate adjustment