Introduction to Causal Inference: Philosophy, Framework and Key Methods

  • Date: 06/08/2016
Speaker(s):
Dr. Erica Moodie (PhD, University of Washington) is a William Dawson Scholar, an Associate Professor of Biostatistics, and Director of the Biostatistics Program in the Department of Epidemiology, Biostatistics, and Occupational Health at McGill University. Her main research interests are in causal inference and longitudinal data analysis with a focus on dynamic treatment regimes. She is an Elected Member of the International Statistical Institute, an Associate Editor of Biometrics and the Journal of the American Statistical Association. She holds a Chercheur-Boursier junior 2 career award from the Fonds de recherche du Quebec-Sante. Dr. Moodie is co-author of the monograph “Statistical Methods for Dynamic Treatment Regimes: Reinforcement Learning, Causal Inference, and Personalized Medicine.”
Location: 

University of Calgary

Description: 

Background:

In health research, we are often interested in knowing how effective treatments are and how destructive exposures are. A deep understanding of the research questions greatly depends on the valid evaluation of the research conducted and the correct interpretation of the data collected. Randomized controlled trials (RCTs), where subjects are randomly allocated to treatments and exposures, are the gold standard in these settings; however, RCTs may not be feasible and affordable in most cases due to ethics and cost issues. Causal inference tackles the challenges of confounding in observational studies and provides novel statistical methods to draw causal conclusions based on observational data in a comprehensive framework. The proposed workshop on causal inference is thus closely aligned with the thrust of AIHS’s for its Community Engagement and Knowledge Grant Program.

 

Aims:

The main objective of the 1-day workshop is to introduce causal inference to the diverse community of researchers at the University of Calgary to strengthen the university’s research strengths in medicine, epidemiology and public health. It will also serve as a forum for the presentation of recent advances in the field to facilitate knowledge sharing and stimulate the development of innovative methodologies among methodologically oriented and subject matter-motivated researchers.

Abstracts / Downloads / Reports: 
Introduction_to_Causal_Interference.pdf
Organizers:
  • Hua Shen, PhD, Department of Mathematics and Statistics, University of Calgary 
  • Ying Yan, PhD, Department of Mathematics and Statistics, University of Calgary 
  • Alexander R. de Leon, Department of Mathematics and Statistics, University of Calgary
Other Information: 

Location: Theatre 4 - Health Sciences Centre

 

 

 

Final scientific report here.