Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data
By J. Rohrer. 2017
829 citations recorded by the source corpus.
Correlation does not imply causation; but often, observational data are the only option, even though the research question at hand involves causality. This article discusses causal inference based on observational data, introducing readers to graphical causal models that can provide a powerful tool for thinking more clearly about the interrelations between variables. Topics covered include the rationale behind the statistical control of third variables, common procedures for statistical control, and what can go wrong during their implementation. Certain types of third variables—colliders and mediators—should not be controlled for because that can actually move the estimate of an association away from the value of the causal effect of interest. More subtle variations of such harmful control include using unrepresentative samples, which can undermine the validity of causal conclusions, and statistically controlling for mediators. Drawing valid causal inferences on the basis of observational data is not a mechanistic procedure but rather always depends on assumptions that require domain knowledge and that can be more or less plausible. However, this caveat holds not only for research based on observational data, but for all empirical research endeavors.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/d3df64275f13fdb6c0e3f40b6f77ba69691bc29d
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Citations in the corpus
- Control variables and causal inference: a question of balance (2018, methodology): “…and there are no other confounding factors.1 More complicated structures such as having more than three independent variables or complex causal pathways will only make the issues I raise here more problematic (Elwert & Winship, 2014; Morgan & Winship, 2015; Pearl et al., 2016; Rohrer, 2018).”
- The impact of foreign direct investment, foreign aid and trade on poverty reduction: Evidence from Sub-Saharan African countries (2020, methodology): “To infer causation, statistical models have to be causal models rather than just correlation effect models (Rohrer, 2018).”
- Enhancing Rigor in Quantitative Entrepreneurship Research (2019, methodology): “And although different disciplines have used different notations for seemingly incompatible approaches to causal inference, causal graphs/directed acyclic graphs (DAGs) have been identified as useful tools for identifying the required assumptions for causal inference and thereby helping to unify var”