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

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.

Robust inference in difference-in-differences and event study designs

R & Python code and datasets for Everyday Causal Inference — a free book on experiments, DiD, IV, RDD, and causal time series for business

difference-in-differences in Python

Robust inference in difference-in-differences and event study designs (Stata version of the R package of the same name)

Synthetic difference in differences for Python

Implementing Local Projections Difference-in-Differences (LP-DiD) estimators

Step-by-step causal inference — method selection, assumptions, and robustness checks

A suite of Julia packages for difference-in-differences

fast and flexible Difference-in-Differences

Agent skills that help you publish in the AER faster — identification-first empirics, AEA-compliant replication, Keith-Head intros, R&R rebuttals for AER / AER:Insights / AEJ. | 助你更快发表 AER 论文的 agent skill 栈：识别优先实证、AEA 合规复现、Keith Head 式引言、R&R 审稿回复，覆盖选题到投稿全流程。

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

Causalis - State-of-the-art robust causal inference for experiments and observational data in python

Causal Inference Using Quasi-Experimental Methods

Estimation of Difference-in-Differences Treatment Effects with Staggered Treatment Onset Using Heterogeneity-Robust Two-Way Fixed Effects Regressions

Regression-based multi-period difference-in-differences with heterogenous treatment effects

Lecture slides, video recordings, and coding exercises from the 2024 Northwestern University Causal Inference Workshop. This repository is not affiliated with Northwestern University or the workshop.

Difference-in-Differences analysis of survey data to estimate causal effects

End-to-end AI workflow for economic & finance research: 43 MCP data sources, 47 econometric methods (DID/IV/RD/PSM/GMM), 30 journal templates (JF/JFE/RFS/经济研究/金融研究/管理世界), HITL gates, 3-LLM adversarial review. MIT. Zenodo: 10.5281/zenodo.21262689