16 Causal Inference

Basic ideas and frameworks (simple, potential outcomes, DAGs)
Pitfalls and mistakes (layman’s terms)
The experimental ideal
Non-experimental approaches to causal inference
Dealing with attrition

Some stuff to integrate from here perhaps.

Useful resources:

Causal Inference: What If. R and Stata code for Exercises

The effect – bookdown, looks great, love nick HK

Causal inference: the Mixtape

Useful R Packages for Causal Inference in the Social Sciences

Applied causal analysis with R

Mostly Harmless Econometrics (R code – where was it?)

# Causal Inference {-#causal}


- Basic ideas and frameworks (simple, potential outcomes, DAGs)
- Pitfalls and mistakes (layman's terms)
- The experimental ideal
- Non-experimental approaches to causal inference
- Dealing with attrition


Some stuff to integrate from [here](https://daaronr.github.io/metrics_discussion/caus-inf-obs.html#caus_inf_obs) perhaps.


## Useful resources: {-}

[Causal Inference: What If. R and Stata code for Exercises](https://remlapmot.github.io/cibookex-r/)

[The effect](https://www.theeffectbook.net/) -- bookdown, looks great, love nick HK

[Causal inference: the Mixtape](https://www.scunning.com/mixtape.html)

[Useful R Packages for Causal Inference in the Social Sciences](https://www.dhidalgo.me/post/useful-r-packages-for-causal-inference-in-the-social-sciences/)

[Applied causal analysis with R](https://bookdown.org/paul/applied-causal-analysis/)

Mostly Harmless Econometrics (R code -- where was it?)