Causal Inference [ESP48]

Detailed information about this course:

Description

Facultry: Dr. Sonja Swanson & Dr. Jeremy Labrecque

The goal of many epidemiologic studies is to quantify the causal effect of a treatment (or exposure) on an outcome. In contrast, commonly used statistical methods provide measures of association that may lack a causal interpretation even when the investigator adjusts for all potential confounders in the analysis of a properly designed study.

To eliminate the discordance between the causal goals and the associational methods in epidemiology, it is necessary to a) formally define causal concepts such as causal effect and confounding, b) identify the conditions required to estimate causal effects, and c) use analytical methods that, under those conditions, provide estimates that can be endowed with a causal interpretation. This course combines counterfactual theory and graph theory to present an integrated framework for causal inference from observational data, with a special emphasis on complex longitudinal data. Specifically, the course will introduce g-methods (inverse probability weighting of marginal structural models; parametric g-formula; and g-estimation of structural nested models) in the setting of time-fixed treatments and demonstrate inverse probability weighting for addressing causal questions regarding sustained treatment strategies. On the final day, alternative or complementary approaches will be discussed (e.g., instrumental variable approaches; quantitative bias analysis).

Objectives

The student is able to:

• Recognize and formulate well defined questions concerning causal effects;
• Use causal diagrams to represent a priori subject-matter knowledge and assumptions;
• Identify the settings in which conventional methods for data analysis are inadequate;
• Use provided software to estimate causal effects under specified conditions.

Participant profile

The course is intended for health researchers or other data scientists who will use observational studies to estimate causal effects as part of their current or future professional career. Examples include: epidemiologists, (bio-)statisticians, and other clinical or public health researchers.

Assessment

Attendance