A certainly non-exhaustive series of lists
Books:
- Causality (Pearl)
- What If (Hernan)
- Causal Inference: The Mixtape (Cunningham)
- The Effect (Huntington-Klein)
- Causal Analysis: Impact Evaluation and Causal Machine Learning with Applications in R
- Causal Artificial Intelligence: A Roadmap for Building Causally Intelligent Systems
Methods:
- Difference-in-Differences
- E-value
- G-computation
- Interrupted Time Series
- Inverse Probability of Treatment Weighting (IPTW)
- Marginal Structural Models
- Matching
- Propensity Scores
- Survival Analysis
- Targeted Maximum Likelihood Estimate (TMLE)
- Synthetic controls
R packages:
- bootstrap
- causaldata
- CausalImpact
- CBPS
- cobalt
- episensr
- EValue
- ggdag
- MatchIt
- multibias (of course)
- sensemakr
- tipr
- tmle
- WeightIt
Python packages:
- causallib
- causalml
- dowhy
- EconML