machine learning 4 data science: 3 Interpreting regression and causality

Joshua Loftus

3 Interpreting regression and causality

Multiple linear regression does not, by default, tell us anything about causality. But with the right data and careful interpretation we might be able to learn some causal relationships.

Materials

Link	Type	Description
html pdf	Slides	Causality and interpreting regression
Rmd	Notebook	Regression coefficients and causality

To be updated

Preparation

Required reading

Mixtape Sections 1.1, 1.2, and 3.1 up to 3.1.3 (stop before 3.1.4)
MLstory Chapter 9 on Causality, roughly the first half (stop before the section called Experimentation, randomization, potential outcomes)

Supplemental reading

Blog post on “double machine learning” up to the second histogram (note that material after that point is more advanced)
MLstory Chapter 9 on Causality and Chapter 10 on Causal inference in practice

Rerum cognoscere causas

Virgil:

Fortunate, who can know the causes of things

Slides, notebooks, exercises

[Slides] for first causality video (PDF)

Slides for second causality video

Slides for logistic regression video

Notebook from seminar

Reuse

Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".

Citation

For attribution, please cite this work as

Loftus (2021, Oct. 9). machine learning 4 data science: 3 Interpreting regression and causality. Retrieved from http://ml4ds.com/weeks/03-causality/

BibTeX citation

@misc{loftus20213,
  author = {Loftus, Joshua},
  title = {machine learning 4 data science: 3 Interpreting regression and causality},
  url = {http://ml4ds.com/weeks/03-causality/},
  year = {2021}
}