Resource Round-Up: Causal Inference

In my post on causal design patterns, I argue that these techniques are currently underutilized in industry because (at least, in part) they are so often couched in domain-specific language. Fortunately, the past few years have seen an explosion of fantastic resources and tools to help practitioners more readily learn and apply these methods. Below, I link some of my favorite readings. To help prioritize which resources (or which chapters within them) will be most relevant to you, please see my previous post for an “advertisement” and overview of some of the main techniques.

Free Books

• Causal Inference: What If by Miguel Hernan and Jamie Robins
  • Written from epidemiology perspective
  • Nice “model free” section introduces causal intuition
  • Code supplement in R, python, SAS, Stata
• Introduction to Causal Inference by Brady Neal
  • Written from ML perspective including “advanced” topics such as Bayesian networks, causal discovery
  • Builds strong theoretical basis with graphical and probabilistic proofs
  • Book complemented by video lectures
• Causal Inference: the Mixtape by Scott Cunningham
  • Written from economics perspective
  • Provides great insights into the history and relevance of different methods in economics literature
  • Includes interactive R code chunks to run as you read
• The Effect: An Introduction to Research Design and Causality by Nick Huntington-Kline
  • Written from the economics perspective
  • Takes a holistic approach to research design with rich examples from literature
• Impact Evaluation in Practice by Gertler, Martinez, Premand, Rawlings, Vermeersch of the World Bank
• Handbook of Field Experiments by Ahbijit Banerjee and Esther Duflo
  • Technically a type of experimentation not causal inference
  • However, the real world challenges of field (versus clinical) research creates some nice “blended” methodologies.
  • For example encouragement designs are closely related to instrumental variable methods. These may have been inadvertently conducted in your business strategy and be available in historical data.

Course Material

• Introduction to Causal Inference by Brady Neal
  • ML perspective including Bayesian networks, causal discovery
  • Slides and video lectures to go along with book linked above
• Program Evaluation by Andrew Heiss
  • Public policy perspective
  • Slides and assignments
• Causal Inference (Propensity Score) Tutorial from UseR!2020 by Lucy D’Agostino McGowan and Malcolm Barrett
  • Epidemiology perspective focused on propensity-score methods
  • Video tutorial with R code on GitHub

Survey Papers & Blogs

Surveys

• Using Causal Inference to Improve the Uber User Experience on the Uber Engineering blog
• The Impact of Machine Learning on Economics by Susan Athey
• The State of Applied Econometrics - Causality and Policy Evaluation by Susan Athey and Guido Imbens

Deeper Dives

• Curated List of Recent Advances by Christine Cai
• Policy Evaluation in COVID: fantastic survey of diff-in-diff and event study methods by Noah Haber, et al

Propensity Score Focused

• Understanding propensity score weighting by Lucy D’Agostino McGowan
• To Balance or Not To Balance by Ivan Diaz and Joseph Kelly (Unofficial Google Data Science Blog)

Miscellaneous Advanced Topic Talks

• Causal Science 2020 Meeting talks
• Synthetic Control lecture by Alberto Abadie
• Heterogenous Treatment Effects lecture by Susan Athey
• Personalized Treatment Effect Estimation by Heidi Seibold

Other Introductory Books

• Learning Microeconometrics with R by Christopher Adams
• Mastering Metrics and Mostly Harmless Econometrics by Joshua Angrist and Jorn-Steffen Pischke
• The Book of Why by Judea Pearl