Learning Resource: Causal Inference

Difference between Econometrics & Statistics

What is the difference between Econometrics and Statistics? Professor Joshua Angrist (MIT) explains the difference in this video.

Causal Inference (theory) learning resources

• Golub Capital Social Impact Lab (2023). Machine Learning-based Causal Inference Tutorial.

  • textbook
  • YouTube tutorial
  • Key idea to remember
    • FWL Theorem ➜ Robinson’s Transformation ➜ R-learner (with R loss)
    • See STATS 361: Causal Inference, page 36 for details.

      

    • For R-learner, Nie, Xinkun, and Stefan Wager. Quasi-oracle estimation of heterogeneous treatment effects. Biometrika provides a good summary.

• Peng Ding’s textbook A first course in causal inference.

• Wager, S. (2022). STATS 361: Causal Inference. Lecture notes, Stanford University.

• Mastering ‘Metrics, less theoretical.

• Causal Inference for The Brave and True is an open-source resource primarily focused on econometrics and the statistics of science. 

• Causal Inference - Statistical Science - Duke University

  • The slides are provided by Professor Fan Li.

  • I highly recommend to read these slides as summary to get a big picture and read Peng Ding’s textbook for details and rigorous proofs.

• Introduction to Causal Inference (Fall 2020) by Brady Neal

  • Brady kindly provides all his course material and YouTube tutorials. They are super helfpul!!!

Causal Inference in R

• R 📦 CRAN Task View: Causal Inference.
• Propensity Score Weighting tutorial: Generating inverse probability weights for both binary and continuous treatments. In this tutorial, the author introduces ipw and WeightIt 📦s.
• rlearner for Quasi-Oracle Estimation of Heterogeneous Treatment Effects.
• DoubleML — DoubleML documentation R 📦. Paper: DoubleML - An Object-Oriented Implementation of Double Machine Learning in R
• Causal Inference in R is a bookdown tutorial. It is new and incomplete.
• grf package for generalized random forests.

Causal Inference in Python

• 🐍 EconML User Guide, this is for double machine learning.

Difference in Difference

• Here is the shared Dropbox from Professor Jeffrey Wooldridge: share_jeff_wooldridge_did

• Here is a link to a list of Stata packages that cover the recent literature on staggered DiD designs: https://asjadnaqvi.github.io/DiD/docs/01_stata/

• YouTube Tutorial: Jeff Wooldridge presents “Differences in Differences” to the ASA Ann Arbor Chapter. Professor Wooldridge provides us a clever transformation that enable us to convert the panel data to cross-sectional data, then one can apply their favorite treatment effect estimators such as matching, IPW, AIPW and etc. Specifically,

  • Identical to regression adjustment (RA) on a transformed variable:
  $$ \dot{Y}_{i t}=Y_{i t}-\frac{1}{(q-1)} \sum_{s=1}^{q-1} Y_{i s}, \quad t \geq q $$
  • For any treatment period $t \geq q$, apply standard treatment effect methods to
  $$ \left\{\left(\dot{Y}_{i t}, D_i, \mathbf{X}_i\right): i=1, \ldots, N\right\} $$

  • Inverse probability weighting

  • IPWRA (Doubly Robust)

  • Covariate or PS matching

  For more details, please check the paper: A Simple Transformation Approach to Difference-in-Differences Estimation for Panel Data

Synthetic Control

• YouTube tutorial: How To Use The Synthetic Control Method in R Step-By-Step: Effect of California’s Tobacco Program

• YouTube tutorial: Synthetic Control Method. This tutorial is short but provides the key insights.

• https://carlos-mendez.quarto.pub/r-synthetic-control-tutorial/

• Good summary for synthetic did (sdid). In this post, it compares the DID, SC and SDID