Randomization and matching
Content for the week of Monday, October 5, 2020–Friday, October 9, 2020
Readings
- Andrew Heiss, “Causal Inference,” Chapter 10 in R for Political Data Science: A Practical Guide (forthcoming) (Ignore the exercises!). Get the PDF here.
- Chapter 4 in Impact Evaluation in Practice Paul J. Gertler et al., Impact Evaluation in Practice, 2nd ed. (Inter-American Development Bank; World Bank, 2016), https://openknowledge.worldbank.org/handle/10986/25030.
- Chapter 1 in Mastering ’Metrics Joshua D. Angrist and Jörn-Steffen Pischke, Mastering ’Metrics: The Path from Cause to Effect (Princeton, NJ: Princeton University Press, 2015).
- 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.
View all slides in new window Download PDF of all slides
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: