Chapter 1: Causal Inference in Randomized and Non-Randomized Studies: The Definition, Identification, and Estimation of Causal Parameters Next Chapter

Michael E. Sobel

In: The SAGE Handbook of Quantitative Methods in Psychology

Chapter 1: Causal Inference in Randomized and Non-Randomized Studies: The Definition, Identification, and Estimation of Causal Parameters

  • Citations
  • Add to My List
  • Text Size

Causal Inference in Randomized and Non-Randomized Studies: The Definition, Identification, and Estimation of Causal Parameters
Causal inference in randomized and non-randomized studies: The definition, identification, and estimation of causal parameters
Introduction

The distinction between causation and association has figured prominently in science and philosophy for several hundred years at least, and, more recently, in statistical science as well, indeed, since Galton, Pearson and, Yule developed the theory of correlation.

Statisticians have pioneered two approaches to causal inference that have proven influential in the natural and behavioral sciences. The oldest dates back to Yule (1896), who wrote extensively about ‘illusory’ correlations, by which he meant correlations that should not be endowed with a causal interpretation. To distinguish between the illusory and non-illusory correlations, Yule invented partial correlation to ‘control’ ...

Looks like you do not have access to this content.

Login

Don’t know how to login?

Click here for free trial login.

Back to Top

Copy and paste the following HTML into your website