Synthetic Control Python Packages

diff-diff

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

statspai

StatsPAI is the first Agent-native Python library for causal inference and applied econometrics — unified API, broad cross-method coverage, structured result objects, machine-readable schemas, Skills, an MCP server, and R/Stata parity validation.

syntheticcontrolmethods

A Python package for causal inference using Synthetic Controls

geoexp

Geo-experiment design on top of augsynth-py: market selection, power-based design ranking, and experiment reporting

scpi-pkg

Synthetic Control Methods

insurance-causal-policy

SDID for causal rate change evaluation in insurance pricing — event study, HonestDiD sensitivity, FCA evidence pack (170 tests)

scspill

Bayesian Synthetic Control with Spillovers in Python

factor-factory

Domain-agnostic factor-model + analysis-pipeline framework with Protocol-based pluggable engines (DiD, SDID, SCM, RDD, survival, event study, mediation, het-TE, …) and first-class jellycell tearsheet integration.

mlsynth

This is the repository for the Python library mlsynth

decisive

Causal inference that returns a verdict, not a number: picks the quasi-experimental design (DiD, staggered adoption, synthetic control, ITS), tests its assumptions, and refuses when they fail. pip install decisive