Linear Models: A Useful “Microscope” for Causal Analysis
On 13 Jun, 2013 By admin 0 Comments
This note reviews basic techniques of linear path analysis and demonstrates, using simple examples, how causal phenomena of non-trivial character can be understood, exemplified and analyzed using diagrams and a few algebraic steps. The techniques allow for swift assessment of how various features of the model impact the phenomenon under investigation. This includes: Simpson’s paradox, case–control bias, selection bias, missing data, collider bias, reverse regression, bias amplification, near instruments, and measurement errors.