jroessler / autoumLinks
A Python Framework for Automatically Evaluating various Uplift Modeling Algorithms to Estimate Individual Treatment Effects
☆23Updated last year
Alternatives and similar repositories for autoum
Users that are interested in autoum are comparing it to the libraries listed below
Sorting:
- ☆103Updated 4 years ago
- 💉📈 Dose response networks (DRNets) are a method for learning to estimate individual dose-response curves for multiple parametric treatm…☆88Updated 2 years ago
- ➕➕ Perfect Match is a simple method for learning representations for counterfactual inference with neural networks.☆129Updated 2 years ago
- Extensive tutorials for learning how to build deep learning models for causal inference (HTE) using selection on observables in Tensorflo…☆330Updated 9 months ago
- An open-source package of causal feature selection and causal (Bayesian network) structure learning (C/C++ version)☆60Updated 4 years ago
- Data derived from the Linked Births and Deaths Data (LBIDD); simulated pairs of treatment assignment and outcomes; scoring code☆84Updated 7 years ago
- EconML/CausalML KDD 2021 Tutorial☆161Updated last year
- Sklearn-style implementations of Neural Network-based Conditional Average Treatment Effect (CATE) Estimators.☆142Updated last year
- A curated list of awesome work on causal inference, particularly in machine learning.☆105Updated 4 years ago
- Codebase for GANITE: Estimation of Individualized Treatment Effects using GANs - ICLR 2018☆61Updated 5 years ago
- pyCausalFS:A Python Library of Causality-based Feature Selection for Causal Structure Learning and Classification☆70Updated 4 years ago
- 💊 Comparing causality methods in a fair and just way.☆139Updated 5 years ago
- Code and documentation for experiments in the TreeExplainer paper☆186Updated 5 years ago
- Code for the WSDM '20 paper, Learning Individual Causal Effects from Networked Observational Data.☆75Updated 4 years ago
- Software and pre-processed data for "Using Embeddings to Correct for Unobserved Confounding in Networks"☆56Updated 2 years ago
- ☆272Updated 3 years ago
- (ICML2020) “Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models’’☆31Updated 2 years ago
- ☆34Updated 3 years ago
- 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
- Tensorflow 2 implementation of Causal-BERT☆71Updated last year
- 4th Year project aiming to implement PC, FCI and RFCI algorithms in python☆14Updated 6 years ago
- ☆58Updated 3 years ago
- Code for ICLR 2020 paper: "Estimating counterfactual treatment outcomes over time through adversarially balanced representations" by I. B…☆59Updated last year
- Positive-unlabeled learning with Python.☆236Updated last week
- ☆22Updated 3 years ago
- Code for NeurIPS 2020 paper: "Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks" by I. Bica…☆30Updated 4 years ago
- Realistic benchmark for different causal inference methods. The realism comes from fitting generative models to data with an assumed caus…☆77Updated 4 years ago
- Code for TEDVAE, a VAE-based treatment effect estimation algorithm.☆25Updated 2 years ago
- Code for ICML 2020 paper: "Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders" by I. …☆52Updated 4 years ago
- DATE: Dual Attentive Tree-aware Embedding for Customs Frauds Detection☆62Updated 4 years ago