A Python package for causal inference using Synthetic Controls

Difference-in-Differences causal inference in Python. Callaway-Sant'Anna, Synthetic DiD, Honest DiD, event studies. sklearn-like API, validated against R.

A curated list of awesome machine learning libraries for marketing, including media mix models, multi touch attribution, causal inference and more

Synthetic difference in differences for Python

Factor-Based Imputation for Missing Data

Prediction and inference procedures for synthetic control methods with multiple treated units and staggered adoption.

A Penalized Synthetic Control Estimator for Disaggregated Data (JASA, 2021)

fast synthetic control estimators for panel data problems

Causal Inference Using Quasi-Experimental Methods

data repository for publicly available code and data of "Low-intensity fires mitigate the risk of catastrophic wildfires in California's forests"

Replication materials for the paper "Evaluating the Effect of Homicide Prevention Strategies in São Paulo, Brazil: A Synthetic Control Approach" (2016)

Tutorials for the synthetic control method for causal inference using PyMC

Replication code for "RNN-based counterfactual prediction, with an application to homestead policy and public schooling"

Replication code for "State-Building through Public Land Disposal? An Application of Matrix Completion for Counterfactual Prediction"

Comparative Case Studies using The Synthetic Control Method

Synthetic DiD estimation in staggered adoption settings

Source code to replicate the results of our article published in Revista de Historia Económica: Another case of the middle-income trap: Chile, 1900-1939

Prevalence of Cannabis Use Disorder Post Legalization – Uruguay Case Study (Synthetic Control Method)

julia implementation of Synthetic Control estimation

A replication of the paper "Democratization and Economic Output in Sub-Saharan Africa".