# 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

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}
}