Causal inference

Causal inference is the task of drawing conclusions from data about the effects of treatments and other type of interventions. In epidemiology and clinical research, as well as in many other fields, formal methods for causal inference play an increasingly central role. This course gives an introduction to basic concepts and ideas in this area.

Among the topics being covered are:

• randomization and target trials,
• counterfactuals and estimands,
• causal directed acyclic graphs (DAGs),
• methods for confounding adjustment,
• marginal structural models and time-dependent confounding,
• causal mediation analysis,
• causal inference in survival analysis.

The area of causal inference has over the last decades grown to be a very active area within statistics. Various new methods have been and are being developed, based on the seminal work by Donald Rubin, James Robins, Judea Pearl and others. This has led to new understandings of how statistical analysis is an integral part of causal inference and a continuously growing toolbox of methods for addressing causal questions.

In epidemiology and clinical research much knowledge about causal effects comes from statistical studies. The new tools give a more precise way of approaching these issues and can help researchers avoid common pitfalls. This course aim to make the participants acquainted with these methodological developments, both for the purpose of performing own research and for assessing the evidence from studies of treatment effects.