uc-berkeley-i-school / causality_reading

This repository supports the "Causality Reading Group" that meets generally every other Monday, at 12:00pm Pacific Time, at this room. Coordination happens in the #field-experiments channel of the ISchool Slack.

What we have been reading

Recursive Partitioning for Heterogeneous Causal Effects

[This paper] is by Athey and Imbens (2016), and is the starting point for a line of research that is taken up with Stephan Wager and others.

Related material:

• ["Machine Learning & Causal Inference: A Short Course"] by Athey and Wager
• Generalized Random Forests: ...
• Causal Trees: ...

Difference-in-differences with variation in treatment timing

Goodman-Bacon. 2019. Difference-in-Differences with Variation in Treatment Timing (later preprint version, published version)

Slides from the meeting (covering some related material)

DoubleML

V. Chernozhukov, D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, and a. W. Newey. Double/Debiased Machine Learning for Treatment and Causal Parameters

Related material:

• https://scholar.princeton.edu/sites/default/files/bstewart/files/felton.chern_.slides.20190318.pdf (“some tutorial slides I developed for a reading group where we dug into the paper”, 2018 - provides a high-level overview and motivates Chernozhukov et al.'s construction via the Frisch-Waugh-Lovell theorem)
• https://matheusfacure.github.io/python-causality-handbook/22-Debiased-Orthogonal-Machine-Learning.html (similarly argues that "Double/Debiased ML can be seen as Frisch, Waugh and Lovell theorem on steroids")
• https://docs.doubleml.org/stable/guide/basics.html

See also

• planning doc
• "Role-Playing Paper-Reading Seminars"