Thanks for reaching out, at2014 and others! Following up here with some resources we’ve found useful:

General:

• Mostly Harmless Econometrics by Angrist & Pischke — a must-read practioner’s guide to causal inference.

Controlled Regression (including with Fixed Effects):

• Data Analysis Using Regression and Multilevel/Hierarchical Models by Gelman — see especially Chapters 9 and 10.

Regression Discontinuity Design:

• “Regression Discontinuity Designs: A Guide to Practice” by Imbens and Lemieux in the Journal of Econometrics
• “Identification and Estimation of Treatment Effects with a Regression Discontinuity Design” by Hahn, Todd, and van der Klaauw in Econometrica

Difference-in-Differences

• “Semiparametric Difference-in-Difference Estimators” by Abadie in Review of Economic Studies

Instrumental Variables:

• “Identification of Causal Effects Using Instrumental Variables” by Angrist, Imbens, and Rubin in the Journal of the American Statistical Association
• “Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments” by Angrist and Krueger in the Journal of Economic Perspectives

Extensions:

• “Lectures on Machine Learning” by Athey and Imbens in 2015 NBER Lecture Series — discusses the nascent intersection of machine learning methods with causal inference
• “Something to Talk About: Social Spillovers in Movie Consumption” by Gilchrist and Sands forthcoming in Journal of Political Economy — a paper we wrote in grad school using IV (interacted with a little machine learning) to understand peer effects in consumption

We also put sample R code for these causal methods, along with basic simulation results, in a Jupyter notebook on GitHub. There are a number of helpful R libraries for causal inference, including AER and rdd, that we use there and are worth checking out.

Happy reading!

Emily & Duncan