LARC Research Seminar

Causality and Learning

Prof Kun ZHANG

Assistant Professor
Carnegie Mellon University


Event Details

26 July 2018, Thursday
9.30am – 5.30pm

Please note that the venue has been changed to:
Seminar Room B1-1
School of Information Systems
Singapore Management University
80 Stamford Road
Singapore 178902

We look forward to seeing you at this research seminar.

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Registration Ends 23 July


Does smoking cause cancer? Can we find the causal direction between two variables by analyzing their observed values? In our daily life and science, people often attempt to answer such causal questions, for the sake of understanding and manipulating systems properly. On the other hand, we are also often concerned with how to do machine learning in complex environments, such as learning under data heterogeneity. For instance, how can we make optimal predictions in non-stationary environments? In the past decades, interesting advances were made in machine learning, statistics, and philosophy for tackling long-standing causality problems, including how to discover causal knowledge from purely observational data and how to infer the effect of interventions using such data. Furthermore, it has recently been shown that causal information can facilitate understanding and solving various machine learning problems, including transfer learning and semi-supervised learning. This tutorial reviews essential concepts in causality studies and is focused on how to learn causal relations from observation data and why and how the causal perspective helps in machine learning and other tasks.


  • 09.00am - 09.30am - Registration and Breakfast
  • 09.30am - 12.00pm - Part I, Part II and Part III
  • 12.00pm - 01.00pm - Lunch
  • 01.00pm - 03.30pm - Part IV, Part V and Part VI
  • 03.30pm - 04.00pm - Tea and Refreshment
  • 04.00pm - 05.30pm - Part VII and Part VIII


  • Part I: Causal thinking
  • Part II: Preliminaries (probability and graphical models)
  • Part III: Identification of causal effects
  • Part IV: Causal discovery 1--Traditional constraint- and score-based methods
  • Part V: Causal discovery 2--Linear, non-Gaussian models
  • Part VI: Causal discovery 3--Nonlinear models and noiseless cases
  • Part VII: Causal discovery 4--Practical issues to address
  • Part VIII: Causality-based machine learning

About Speaker

Kun Zhang is an assistant professor in the philosophy department and an affiliate faculty in the machine learning department of Carnegie Mellon University (CMU), USA, as well as a senior research scientist at Max Planck Institute for Intelligent Systems, Germany. Before joining CMU, he was a lead scientist at Information Sciences Institute of University of Southern California for one year. His research interests lie in machine learning and artificial intelligence, especially in causal discovery and causality-based machine learning. He has being serving as an area chair or senior program committee member for many conferences in machine learning or artificial intelligence, including UAI, ICML, NIPS, AAAI, IJCAI, AIStats, and ICDM, and as an associate editor for several journals. He has organized various academic activities to foster interdisciplinary research in causality.

National Research Foundation

This research is supported by the National Research Foundation, Prime Minister's Office, Singapore under its International Research Centres in Singapore Funding Initiative.

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