Causal inference from observational data

This study provides an overview of state‐of‐the‐art methods specifically designed for causal inference in observational data, including difference‐in‐differences (DiD) analyses, instrumental variables (IV), regression discontinuity designs (RDD) and fixed‐effects panel data analysis. The described methods may be particularly useful in dental research, not least because of the increasing availability of routinely collected administrative data and electronic health records (‘big data’).
Source: Community Dentistry and Oral Epidemiology - Category: Dentistry Authors: Tags: Commentary Source Type: research