Causal-Inference

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Causal-Inference

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Causal Inference Mixtape Sessions

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  - ri.do
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- Angrist and Krueger 1991.pdf
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- Athey and Imbens 2016 randomization inference.pdf
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- Bellemare Bloem and Wexler FDC 2020.pdf
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- Calaway and Santanna 2020.pdf
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- Cattaneo et al. 2019.pdf
- Cengiz et al 2019.pdf
- Cheng and Hoekstra 2013.pdf
- Cook 2008.pdf
- Dell and Querebin 2018.pdf
- from late to mte.pdf
- Fuzzy DD Restuyd.pdf
- Hansen AER 2015.pdf
- Heckman et al 1997.pdf
- Imbens & Angrist - LATE 1994.pdf
- Imbens and Rubin 2015 SUTVA.pdf
- J Policy Anal Manage - 2020 - Ferman - Cherry Picking with Synthetic Controls.jpg
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- Kolesár and Rothe AER 2018.pdf
- McCrary 2008.pdf
- Morgan and Winship SUTVA.pdf
- Planned schedule.docx
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- Sun and Abraham 2020.pdf
- Thornton 2008.pdf
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- 00-Hidden_curriculum.pdf
- 01-Foundations.pdf
- 02-Covariates.pdf
- 02-RDD.pdf
- 03-ATTGT.pdf
- 03-IV.pdf
- 04-Stacking.pdf
- 04-TWFE.pdf
- 05-DID.pdf
- 05b-DiD Covariates.pdf
- 06-Case_Studies.pdf
- 06-Synthetic.pdf
- 07-Matching_and_Weighting.pdf
- DD_01-Basics.pdf
- instruments.pdf
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README.log
README.md

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This workshop will cover foundational elements of modern practices of causal inference such as the potential outcomes model as well as discuss in detail the most common designs: regression discontinuity, instrumental variables, difference in differences, comparative case studies using synthetic control and if time permitting matching. It will be accompanied by efforts to introduce students to basic practices in programming as well as good research practices more generally.

<details><summary>Hidden Curriculum</summary>

About
 
While causal inference is a design and model based approach to estimating causal effects, it ultimately uses large data sources, computers and programming languages to do that estimation. Thus while you can teach causal inference as separate from empirical workflow, you shouldn't. Here I discuss my own personal beliefs about empirical workflow, going through such things as missingness in data, hierarchy of directories, version control and more. We will discuss these things.

Slides
 
<a href="https://github.com/Mixtape-Sessions/Causal-Inference/blob/main/Slides/00-Hidden_curriculum.pdf">Hidden curriculum slides</a>

</details>

<details><summary>Potential Outcomes</summary>

About
 
The modern theory of causality is based on a seemingly simple idea called the "counterfactual". The counterfactual is an unusual features of the arsenal of modern statistics because it is more or less storytelling about alternative worlds that may or may not exist, but could have existed had one single decision gone a different way. Out of this idea grew what a model, complete with its own language, on top of which the field of causal inference is based, and the purpose of this lecture is to learn that language. The language is called potential outcomes and it forms the basis for many causal objects we tend to be interested in, such as the average treatment effect. I also cover randomization, selection bias and randomization inference.

Slides
 
<a href="https://github.com/Mixtape-Sessions/Causal-Inference/blob/main/Slides/01-Foundations.pdf">Foundations of causality</a>

Code
 
* Stata: <a href="https://github.com/Mixtape-Sessions/Causal-Inference/blob/main/Code/ri.do">ri.do</a>, <a href="https://github.com/Mixtape-Sessions/Causal-Inference/blob/main/Code/ri.do">tea.do</a>, <a href="https://github.com/Mixtape-Sessions/Causal-Inference/blob/main/Code/thornton_ri.do">thornton_ri.do</a>
* R: <a href="https://colab.research.google.com/github/scunning1975/mixtape_learnr/blob/main/R_ipynb/Potential_Outcomes.ipynb">Potential outcomes</a>
* python: <a href="https://colab.research.google.com/github/causalinfpython/mixtape/blob/main/Potential_Outcomes.ipynb">Potential outcomes</a>

Readings

Mixtape chapter 4 <a href="https://mixtape.scunning.com/potential-outcomes.html">Potential Outcomes Causal Model</a>
Software: <a href="http://www.dagitty.net">Daggity</a>

</details>

<details><summary>Directed Acyclic Graphs</summary>

About
 
Model-based approaches to identification can be sometimes better seen using causal graphs called directed acyclic graphs (DAGs). These modeling approaches are compatible with the design-based approach, but tend to emphasize a priori domain knowledge as opposed to treatment manipulation exclusively. Here we discuss the backdoor criterion, the frontdoor criterion, and collider bias.

Slides
 
<a href="https://github.com/Mixtape-Sessions/Causal-Inference/blob/main/Slides/01-Foundations.pdf">Foundations of causality</a>

Code
 
* Stata: <a href="https://github.com/Mixtape-Sessions/Causal-Inference/blob/main/Code/moviestar.do">moviestar.do</a>, <a href="https://github.com/Mixtape-Sessions/Causal-Inference/blob/main/Code/collider_discrimination.do">collider_discrimination.do</a>
* R: <a href="https://colab.research.google.com/github/scunning1975/mixtape_learnr/blob/main/R_ipynb/Directed_Acyclical_Graphs.ipynb">DAGs</a>
* python: <a href="https://colab.research.google.com/github/causalinfpython/mixtape/blob/main/Directed%20Acyclic%20Graphs.ipynb">DAGs</a>

Readings
 
Mixtape chapter 3 <a href="https://mixtape.scunning.com/dag.html">Directed Acyclic Graphs</a>

</details>

<details><summary>Sharp Regression Discontinuity</summary>

About
One of the most desired quasi-experimental designs -- desired because it is viewed as highly credible despite being based on observational data -- is the regression discontinuity design. Here I will discuss the sharp RDD in great detail, going through identification, estimation, specification tests and tips, as well as a replication.

Slides
<a href="https://github.com/Mixtape-Sessions/Causal-Inference/blob/main/Slides/02-RDD.pdf">RDD slides</a>

Code
* Stata: lmb.do files (several numbered), rdd_simulate.do (several numbered)
* R: <a href="https://colab.research.google.com/github/scunning1975/mixtape_learnr/blob/main/R_ipynb/Regression_Discontinuity.ipynb">RDD</a>
* python: <a href="https://colab.research.google.com/github/causalinfpython/mixtape/blob/main/Regression_Discontinuity.ipynb">RDD</a>

Readings
Mixtape chapter 6: <a href="https://mixtape.scunning.com/regression-discontinuity.html">Regression discontinuity</a>

</details>

<details><summary>Instrumental Variables</summary>

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<details><summary>Difference-in-Differences</summary>

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<details><summary>Synthetic Control</summary>

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</details>

<details><summary>Matching</summary>

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</details>

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