Causal Effects and Their Estimation: A Practical Workflow, from Planning to Application — Professional Development Continuing Education Course
ASA, Section for Statistical Programmers and Analysts
When does an effect estimate have a causal interpretation and which effect has an interpretation appropriate for your question? This course provides an overview of causal inference that is designed to answer these types of practical questions when data from an observational or nonrandomized study are analyzed. It describes the differences between possible choices for causal estimands, tools for analyzing a data generating process, and statistical methods that support valid effect estimation. It reviews the definition of causal effects in a potential outcomes framework, discusses estimates for total effects, and describes the decomposition of effects through causal mediation analysis, with an emphasis on dichotomous treatments. Directed acyclic graphs (DAGs) are presented as a tool for representing a data generating process, reasoning about possible data generating processes, and constructing valid estimation strategies. For the estimation of treatment effects, this course discusses the appropriate use of propensity score methods, doubly robust methods, and a regression approach to causal mediation analysis. This course provides a review of the theory behind these methods and then focuses on illustrating their application with examples that use SAS/STAT® software. This material demonstrates a rigorous workflow for causal effect estimation. No prior experience with the methods is assumed.
Instructor(s): Clay Thompson, SAS; Michael Lamm, SAS; Yiu-Fai Yung, SAS
