# Measurement and DAGs

Content for Thursday, February 3, 2022

### DAGs

• Julia M. Rohrer, “Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data”3 This will be posted on iCollege.
• Section 2 only (pp. 4–11) from Julian Schuessler and Peter Selb, “Graphical Causal Models for Survey Inference.”4 The PDF is available at SocArXiv.
• Chapters 6 and 7 in The Effect5

## Slides

The slides for today’s lesson are available online as an HTML file. Use the buttons below to open the slides either as an interactive website or as a static PDF (for printing or storing for later). You can also click in the slides below and navigate through them with your left and right arrow keys.

Fun fact: If you type ? (or shift + /) while going through the slides, you can see a list of special slide-specific commands.

## Videos

Videos for each section of the lecture are available at this YouTube playlist.

You can also watch the playlist (and skip around to different sections) here:

## In-class stuff

Here are all the materials we’ll use in class:

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Make sure you modify the metadata of your document to use bookdown when knitting:

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1. Peter H. Rossi, Mark W. Lipsey, and Gary T. Henry, Evaluation: A Systematic Approach, 8th ed. (Los Angeles: Sage, 2019). ↩︎

2. Nick Huntington-Klein, The Effect: An Introduction to Research Design and Causality (Boca Raton, Florida: Chapman and Hall / CRC, 2021), https://theeffectbook.net/. ↩︎

3. Julia M. Rohrer, “Thinking Clearly about Correlations and Causation: Graphical Causal Models for Observational Data,” Advances in Methods and Practices in Psychological Science 1, no. 1 (March 2018): 27–42, doi:10.1177/2515245917745629. ↩︎

4. Julian Schuessler and Peter Selb, “Graphical Causal Models for Survey Inference” (Working Paper, SocArXiv, December 17, 2019), doi:10.31235/osf.io/hbg3m. ↩︎

5. Huntington-Klein, The Effect. ↩︎