Reviews, tutorials, and practical guides to causal inference tools and frameworks, including open-source libraries, experimentation platforms, and applied workflows.
The causalml Package in Python: Uplift Modeling and CATE Meta-Learners
The gsynth Package in R: Generalized Synthetic Control with Interactive Fixed Effects
The npcausal Package in R: Nonparametric Dose-Response Estimation for Continuous Treatments
rdrobust for Regression Kink Design: Estimating Slope Discontinuities with deriv=1
Tigramite in Python: PCMCI for Causal Discovery in Time Series
The bdid Package: Bayesian Difference-in-Differences for Staggered Treatments
The ivmte Package in R: Marginal Treatment Effects and Bounding Policy-Relevant Parameters
The contdid Package in R: Estimating Dose-Response Functions with Continuous Treatments
CausalPy in Python: Bayesian Quasi-Experimental Causal Inference
dagitty and ggdag in R: Drawing and Querying Causal Graphs
The drdid Package in R: Doubly Robust Difference-in-Differences
The Synth Package in R: Implementing the Original Abadie Synthetic Control
The staggered Package in R: Efficient DiD Under Staggered Adoption
lpirfs in R: Estimating Impulse Responses with Local Projections
Microsoft's EconML: Causal Machine Learning in Python
The bacondecomp Package in R: Visualising theGoodman-Bacon Decomposition
The HonestDiD Package in R: Sensitivity Analysis for Difference-in-Differences
synthdid in R: Implementing Synthetic Difference-in-Differences
Double Machine Learning in Practice: The DoubleML Package (R and Python)
Causal Forests in R: The grf Package
The `augsynth` Package in R: Synthetic Control and ASCM
The `did` Package in R: A Complete Workflow
fixest in R: Fast Fixed Effects, Event Studies, and Sun–Abraham DiD
rdrobust in R: Optimal Bandwidth and Robust Inference for RDD