difference-in-differences

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

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

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)

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.

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

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。

A suite of Julia packages for difference-in-differences

fast and flexible Difference-in-Differences

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

Claude Code skills for rigorous social science research: DID, RCTs, regression diagnostics, visualization, literature reviews, and more. Encode methodological best practices directly into your AI coding workflow.

Automatic data-aware staggered-adoption DID analysis in Stata - 8 estimators + full diagnostics via one command or one sentence