andrewtavis / causeinferLinks
Machine learning based causal inference/uplift in Python
☆61Updated last year
Alternatives and similar repositories for causeinfer
Users that are interested in causeinfer are comparing it to the libraries listed below
Sorting:
- ☆17Updated 5 years ago
- Multiple Response Uplift (or heterogeneous treatment effects) package that builds and evaluates tradeoffs with multiple treatments and mu…☆69Updated 2 months ago
- Time Series Forecasting and Imputation☆48Updated 3 years ago
- 🪜 Bayesian Hierarchical Models at Scale☆51Updated 3 years ago
- A framework to infer causality on a pair of time series of real numbers based on Variable-lag Granger causality and transfer entropy.☆56Updated last year
- A Python package which implements several boosting algorithms with different combinations of base learners, optimization algorithms, and …☆63Updated 3 years ago
- Python package for Feature-based Forecast Model Averaging (FFORMA).☆28Updated 5 years ago
- Machine Learning models using a Bayesian approach and often PyMC3☆24Updated 4 years ago
- Bayesian time series forecasting and decision analysis☆116Updated 2 years ago
- Tweedie family density estimation in python☆28Updated last year
- A python package for hierarchical forecasting, inspired by hts package in R.☆28Updated 4 months ago
- Python package for Wald's sequential probability ratio test☆47Updated last year
- The cause2e package provides tools for performing an end-to-end causal analysis of your data. Developed by Daniel Grünbaum (@dg46).☆60Updated 2 months ago
- State Space Estimation of Time Series Models in Python: Statsmodels☆44Updated 8 years ago
- A full example for causal inference on real-world retail data, for elasticity estimation☆50Updated 4 years ago
- 💊 Comparing causality methods in a fair and just way.☆139Updated 5 years ago
- The lite version of the package pydlm. A lite yet powerful Bayesian dynamic modeling library☆12Updated 7 years ago
- Fit Sparse Synthetic Control Models in Python☆83Updated last year
- This repository contains the experiments related with a new baseline model that can be used in forecasting weekly time series. This model…☆47Updated 3 years ago
- Implementation of algorithms from the paper "Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application…☆25Updated 3 years ago
- Comparing different performance estimation methods for time series forecasting tasks☆37Updated 2 months ago
- Logistic regression with bound and linear constraints. L1, L2 and Elastic-Net regularization.☆33Updated 2 years ago
- (Python, R) Cost-sensitive multiclass classification (Weighted-All-Pairs, Filter-Tree & others)☆48Updated 2 months ago
- Data for and description of the ACIC 2023 data competition☆32Updated 2 years ago
- Seminar on Limitations of Interpretable Machine Learning Methods☆57Updated 4 years ago
- R code for ''Bayesian method for causal inference in spatially-correlated multivariate time series''☆46Updated 4 years ago
- Implements the Causal Forest algorithm formulated in Athey and Wager (2018).☆70Updated 5 years ago
- Causal Inference for Time Series Data (with CausalML Demo)☆14Updated 2 years ago
- Repository containing the code of the projects presented in my personal website.☆42Updated last week
- An extension of CatBoost to probabilistic modelling☆145Updated last year