Causal Inference in Epidemiology

About

Causal inference from observational data is a key task of biostatistics and of allied sciences such as sociology, econometrics, behavioral sciences, demography, economics, health services research, etc. These disciplines share a methodological framework for causal inference that has been developed over the last decades. This course presents this unifying causal theory and shows how biostatistical concepts and methods can be understood within this general framework. The course emphasizes conceptualization but also introduces statistical models and methods for causal effect estimation. Specifically, this course strives to a) formally define causal concepts such as causal effect and confounding using potential outcomes and counterfactuals, b) identify the conditions required to estimate causal effects using Directed Acyclic Graphs (DAGs), and c) introduce analytical methods that, under those conditions, provide estimates that can be endowed with a causal interpretation. Examples of such methods are regression adjustment, standardization and inverse probability weighting.

Course leader

Nicholas P. Jewell (Berkeley University)

Target group

Physicians, clinicians and public health professionals from public and private institutions who are looking for systematic training in the principles of epidemiology and biostatistics, or epidemiology applied to health care planning and evaluation. They will acquire familiarity with epidemiological and biostatistical principles and techniques and with the computational tools needed to solve practical problems.

Students in biostatistics and epidemiology, and researchers both from public and private institutions who wish to increase their familiarity with quantitative methods or to deepen their knowledge of a specific area of interest, so they can more effectively address problems in health research. They will gain knowledge in modern, advanced methods useful for health professionals engaged in clinical practice, research and teaching.

Course aim

The course emphasizes conceptualization but also introduces statistical models and methods for causal effect estimation.

Fee info