RMF2010 Abstracts
Session: 65 - Thursday 8th July PM (14.00 - 17.30)
Title: Causal inference
Name: Sylvia Richardson
Affiliation: NCRM BIAS node
Abstract Details
Causal inference is the area of statistical methodology aimed at identifying and estimating effects of interventions and understanding the causal mechanisms that generate the data that we observe.
This session shows the diverse applications of statistical causal research ranging from epidemiology to genetics to social science and economics. Causality as expressed by change models in environmental epidemiology; The role of mendelian randomization in explicating genetic causes; The effect of education on social mobility using path analysis; Using a natural experiment (the 1995 pill scare) and a random discontinuity design to quantify the effect of planned pregnancy on neo-natal birth.
Presentation downloads
Presenter: Michael Joffe
The practical uses of causal diagrams
Presenter: Jouni Kuha
Path analysis for discrete variables: The role of education in social mobility
Presenter: Nuala A Sheehan