co2-emissions

Statsmodels: statistical modeling and econometrics in Python

Lightning ⚡️ fast forecasting with statistical and econometric models.

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.

A directory and analysis of the open source ecosystem in the areas of climate change, sustainable energy, biodiversity and natural resources. https://docs.getgrist.com/gSscJkc5Rb1R/OpenSustaintech

2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.

A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.

ARCH models in Python

Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry through innovation, reduce human bias in the modeling process & build a strong open source marketing science community.

LightweightMMM ? is a lightweight Bayesian Marketing Mix Modeling (MMM) library that allows users to easily train MMMs and obtain channel attribution information.