2 Statistical Inference for Causal Effects, With Emphasis on Applications in Epidemiology and Medical Statistics

This chapter provides an overview of the approach to the estimation of causal effects of treatments from randomized and nonrandomized data based on the concept of potential outcomes and the Bayesian posterior predictive approach.

Abstract

A central problem in epidemiology and medical statistics is how to draw inferences about the causal effects of treatments (i.e., interventions) from randomized and nonrandomized data. For example, does the new drug really reduce heart disease, or does exposure to that chemical in drinking water increase cancer rates relative to drinking water without that chemical? This chapter provides an overview of the approach to the estimation of such causal effects based on the concept of potential outcomes. We discuss randomization-based approaches and the Bayesian posterior predictive approach.