Measurement and DAGs

Content for Monday, September 12, 2022

Readings

Measurement

• The witch trial scene from Monty Python and the Holy Grail
• Chapter 5 in Evaluation: A Systematic Approach (Rossi, Lipsey, and Henry 2019). This is available on iCollege.
• Chapter 5 in The Effect (Huntington-Klein 2021)

DAGs

• Julia M. Rohrer, “Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data” (Rohrer 2018) This will be posted on iCollege.
• Section 2 only (pp. 4–11) from Julian Schuessler and Peter Selb, “Graphical Causal Models for Survey Inference.” (Schuessler and Selb 2019) The PDF is available at SocArXiv.
• Chapters 6 and 7 in The Effect (Huntington-Klein 2021)

DAG example page

• The example page on DAGs shows how to draw and analyze DAGs with both dagitty.net and R + ggdag

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.

View all slides in new window Download PDF of all slides

Introduction

Abstraction, stretching, and validity

Causal models

Paths, doors, and adjustment

Tip

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.

• Introduction
• Abstraction, stretching, and validity
• Causal models
• Paths, doors, and adjustment

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:

• Session 4 FAQ slides (PDF)

References

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

Rohrer, Julia M. 2018. “Thinking Clearly about Correlations and Causation: Graphical Causal Models for Observational Data.” Advances in Methods and Practices in Psychological Science 1 (1): 27–42. https://doi.org/10.1177/2515245917745629.

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

Schuessler, Julian, and Peter Selb. 2019. “Graphical Causal Models for Survey Inference.” Working Paper. SocArXiv. https://doi.org/10.31235/osf.io/hbg3m.