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Why We Should Teach Causal Inference: Examples in Linear Regression With Simulated Data

Karsten Lübke et al.

Journal of Statistics and Data Science Education2020https://doi.org/10.1080/10691898.2020.1752859article
ABDC B
Weight
0.67

What the paper says

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.

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https://doi.org/https://doi.org/10.1080/10691898.2020.1752859

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@article{karsten2020,
  title        = {{Why We Should Teach Causal Inference: Examples in Linear Regression With Simulated Data}},
  author       = {Karsten Lübke et al.},
  journal      = {Journal of Statistics and Data Science Education},
  year         = {2020},
  doi          = {https://doi.org/https://doi.org/10.1080/10691898.2020.1752859},
}

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Why We Should Teach Causal Inference: Examples in Linear Regression With Simulated Data

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Evidence weight

0.67

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.81 × 0.4 = 0.32
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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