Causal Inference Resources
A practical index of causal inference resources for learning, design, estimation, diagnostics, and deployment.
Sections are grouped by workflow need and method family. Every list is in alphabetical order.
1) Foundations and General References
- Causal Inference for Statistics, Social, and Biomedical Sciences (Imbens & Rubin)
- Causal Inference for the Brave and True (Cunningham)
- Causal Inference in Statistics: A Primer (Pearl, Glymour, Jewell)
- Causal Inference: The Mixtape
- Elements of Causal Inference (Peters, Janzing, Schölkopf)
- Introduction to Causal Inference (Hernán & Robins)
- Mastering ‘Metrics (Angrist & Pischke)
- Mostly Harmless Econometrics (Angrist & Pischke)
- The Effect (Huntington-Klein)
2) End-to-End Causal Toolkits (Python / R)
- causalinference (Python)
- CausalML (Python)
- DoWhy (Python)
- EconML (Python)
- grf (R)
- MatchIt (R)
- PyMC Labs Causal Inference Examples
- tmle3 (R)
- WeightIt (R)
3) Causal Graphs, DAGs, and Discovery
- Ananke (Python)
- Causal Discovery Toolbox (Python)
- CausalNex (Python)
- dagitty
- gCastle (Python)
- pgmpy (Python)
- pyAgrum (Python)
- Tetrad
4) Randomized Experiments and Design
- A/B Testing Intuition Busters (Kohavi et al.)
- CUPED at Microsoft
- Designing and Deploying Online Field Experiments (Kohavi, Tang, Xu)
- Interference and Network Experiments (Athey, Eckles, Imbens)
- Switchback Experiments at DoorDash
5) Matching, Weighting, and Doubly Robust Estimation
6) Difference-in-Differences, Event Studies, and Panel Causal Methods
- did (R)
- did2s (R)
- DRDID (R)
- eventStudyInteracts (R)
- fixest (R)
- HonestDiD (R)
- linearmodels (Python)
- PanelMatch (R)
- Synth (R)
- SyntheticControlMethods (Python)
7) Instrumental Variables and Regression Discontinuity
8) Heterogeneous Treatment Effects, Uplift, and Meta-Learners
- Causal Forests / grf
- causal-ml-book (Facure)
- DoubleML
- metalearners (Python)
- Orthogonal Random Forests (EconML)
- Uplift Modeling with CausalML
- X-Learner (Künzel et al., PNAS)