Randomization and matching
Content for Thursday, February 22, 2024
Readings
- Andrew Heiss, “Causal Inference,” Chapter 10 in R for Political Data Science: A Practical Guide (2020) (Ignore the exercises!). Get the PDF here.
- Chapter 4 in Impact Evaluation in Practice (Gertler et al. 2016)
- Chapters 11 and 13 in The Effect (Huntington-Klein 2021). Focus especially on section 13.3 about standard errors. And skim chapter 11; it’s an overview of regression, which we reviewed in session 2, but it also applies the language of DAGs to regression, so look for that specifically as you read.
- Planet Money, “Moving To Opportunity?,” episode 937
- Aaron Carroll, “Workplace Wellness Programs Don’t Work Well. Why Some Studies Show Otherwise,” The Upshot, August 6, 2018
RCTs, matching, and inverse probability weighting
- The example page on RCTs shows how to use R to analyze and estimate causal effects from RCTs
- The example page on matching and inverse probability weighting shows how to use R to close backdoors, make adjustments, and find causal effects from observational data using matching and inverse probability weighting
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.
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
- The magic of randomization
- How to analyze RCTs
- The “gold” standard
- Adjustment with matching
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:
Other helpful resources:
- “The Impact of Mask Distribution and Promotion on Mask Uptake and COVID-19 in Bangladesh”
- Macartan Humphreys, “I saw your RCT and I have some worries! FAQs”
- Darren Dahly, “Out of balance: A perspective on covariate adjustment in randomized experiments”
- Bayesian stats and decison making
- Standard errors
- Unobserved confounding and sensitivity analysis
References
Gertler, Paul J., Sebastian Martinez, Patrick Premand, Laura B. Rawlings, and Christel M. J. Vermeersch. 2016. Impact Evaluation in Practice. 2nd ed. Washington, DC: Inter-American Development Bank and World Bank. https://doi.org/10.1596/978-1-4648-0779-4.
Huntington-Klein, Nick. 2021. The Effect: An Introduction to Research Design and Causality. Boca Raton, Florida: Chapman and Hall / CRC. https://theeffectbook.net/.