Measurement and DAGs

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.
• Lucy D’Agostino McGowan, Travis Gerke, and Malcolm Barrett, “Causal inference is not just a statistics problem” (D’Agostino McGowan, Gerke, and Barrett 2024)
• Zachary M. Laubach et al., “A Biologist’s Guide to Model Selection and Causal Inference” (Laubach et al. 2021). This will be posted on iCollege.
• 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

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)
• Statistical vs. substantive significance
• Statistical testing in null worlds
• Nick Huntington‐Klein et al., “The Influence of Hidden Researcher Decisions in Applied Microeconomics,” Economic Inquiry 59, no. 3 (2021): 944–60, https://doi.org/10.1111/ecin.12992.
• Complex DAG example
• Dolly Parton’s Imagination Library + video intro
• A dubious DAG

Logic model and ladder of abstraction for Imagination Library

Part 1 (inputs and activities)

Part 2 (outputs and outcomes)

Part 3 (ladder of abstraction)

                     Presence of books in the house
                          Position of books
           Quality of books - new books - library participation
      Time spent reading                        Story time
Average hours of parents reading            # of books checked out
     Words spoken at home

Confounding, unblocked

Confounding, blocked

Mediation

Collider bias

References

D’Agostino McGowan, Lucy, Travis Gerke, and Malcolm Barrett. 2024. “Causal Inference Is Not Just a Statistics Problem.” Journal of Statistics and Data Science Education 32 (2): 150–55. https://doi.org/10.1080/26939169.2023.2276446.

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

Laubach, Zachary M., Eleanor J. Murray, Kim L. Hoke, Rebecca J. Safran, and Wei Perng. 2021. “A Biologist’s Guide to Model Selection and Causal Inference.” Proceedings of the Royal Society B 288 (1943). https://doi.org/10.1098/rspb.2020.2815.

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.