The Colloquium on When Causal Inference meets Statistical Analysis aims to encourage and highlight novel approaches to handle the challenges on causal inferences, causal discovery, causal analysis of machine learning. The colloquium will explore topics related to, but not limited to:

• Causal discovery
• Causal learning and control problems
• Theoretical foundation of causal inference
• Causal inference and active learning
• Causal learning in low data regime
• Reinforcement learning
• Causal machine learning
• Causal generative models
• Benchmark for causal discovery and causal reasoning

This colloquium will several keynote speakers including Antoine Chambaz, Eric Gaussier, Yingzhen Li, Elina Robeva, Chandler Squires, Bin Yu, among others. 

Important Dates

Paper Submission Deadline: February 9th, 2023 (23:59 AOE)
 Notification to Authors: February 28th, 2023
Colloquium: From April 17th to April 21st, 2023

More information : https://quarter-on-causality.github.io/analysis/

Image credit to Rice University