{"repo":"igerber/diff-diff","free":true,"listed":false,"github":"https://github.com/igerber/diff-diff","clone":"git clone https://github.com/igerber/diff-diff.git","description":"Difference-in-Differences causal inference in Python. Callaway-Sant'Anna, Synthetic DiD, Honest DiD, event studies. sklearn-like API, validated against R.","language":"Python","stars":378,"topics":["analytics","causal-inference","difference-in-differences","econometrics","economics","data-science","did","event-study","panel-data","sensitivity-analysis"],"license":"MIT","category":"analytics","readme_excerpt":"diff-diff A Python library for Difference-in-Differences (DiD) causal inference - sklearn-like estimators with statsmodels-style outputs, built for econometricians, marketing analysts, and data scientists running campaign-lift, policy, and staggered-rollout analyses. Installation For development: Quick Start Documentation - Quickstart - basic 2x2 DiD with column-name and formula interfaces, covariates, fixed effects, cluster-robust SEs - Choosing an Estimator - decision flowchart for picking the right estimator - Tutorials - hands-on Jupyter notebooks covering every estimator and design pattern - Troubleshooting - common issues and solutions - R Comparison Python Comparison Benchmarks - validation results vs did , synthdid , fixest - API Reference - full API for all estimators, results classes, diagnostics, utilities For AI Agents If you are an AI agent or LLM using this library, call diff diff.get llm guide() for a concise API reference with an 8-step practitioner workflow (based on Baker et al. 2025). The workflow ensures rigorous DiD analysis - testing assumptions, running sensitivity analysis, and checking robustness, not just calling fit() . The guides are bundled in the wheel - accessible from a pip install with no network access. After estimation, call practitioner next steps(results) for context-aware guidance on remaining diagnostic steps. For Data Scientists Measuring campaign lift? Evaluating a product launch? Rolling out a policy in waves? diff-diff handles the ca","default_branch":null,"files":null,"tree":[],"storefront":"/r/igerber","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/igerber/diff-diff/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}