Learning Resource: Causal Inference

Dec 14, 2023 3 min read causal inference, ML

Difference between Econometrics & Statistics

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

Statisticians use sampling to make statistical inferences about large populations. Econometricians, on the other hand, examine counterfactuals to make causal inferences.

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!!!

Casual 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.

Casual Inerence 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