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