Difference In Differences 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.

differences

difference-in-differences in Python

finai-research-workflow

Evidence-first AI workflow for economic and financial research: literature → identification → data → econometrics → verifiable LaTeX. 43 data sources, 58 method modules, 18 AI skills, 30 journal templates.

stataflow

Stata-aligned econometrics for Python, with field-level validation across the documented support surface.

moderndid

Scalable, GPU-accelerated Python library for modern difference-in-differences.

insurance-causal-policy

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

diddesign

Double Difference-in-Differences for Python — GMM-optimal combination of multiple pre-treatment periods with bootstrap inference, diagnostic tools, and publication-ready plotting (Egami & Yamauchi, 2023).

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.

equitrends

Equivalence tests for pre-trends in Difference-in-Differences estimation. Implements max, mean, and RMS tests from Dette & Schumann (2024, JBES).

did-imputation

Borusyak-Jaravel-Spiess (2024) difference-in-differences imputation estimator with event-study plots for Python.

sctrial

Trial-Aware Statistical Inference for Single-Cell Data

pretest-did

Python implementation of the conditional extrapolation pre-test for difference-in-differences (Mikhaeil & Harshaw, 2026). Returns pass/fail decision, conditional confidence interval, and diagnostic scalars as a typed result object.

contdid

Continuous treatment difference-in-differences estimation for Python — dose-response curves, event-study dynamics, and uniform confidence bands.

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