Why We Should Teach Causal Inference: Examples in Linear Regression With Simulated Data
Karsten Lübke, Matthias Gehrke, Jörg Horst, Gero Szepannek
Abstract
Basic knowledge of ideas of causal inference can help students to think beyond data, that is, to think more clearly about the data generating process. Especially for (maybe big) observational data, qualitative assumptions are important for the conclusions drawn and interpretation of the quantitative results. Concepts of causal inference can also help to overcome the mantra “Correlation does not imply Causation.” To motivate and introduce causal inference in introductory statistics or data science courses, we use simulated data and simple linear regression to show the effects of confounding and when one should or should not adjust for covariables.
Topics & Concepts
Causal inferenceInferenceCausationComputer scienceMantraInterpretation (philosophy)Fiducial inferenceCausality (physics)Causal modelRegressionObservational studyEconometricsFrequentist inferenceArtificial intelligenceStatisticsMathematicsBayesian inferenceEpistemologyBayesian probabilityTheologyPhysicsProgramming languagePhilosophyQuantum mechanicsAdvanced Causal Inference TechniquesStatistics Education and MethodologiesStatistical Methods in Clinical Trials