hlzhang109 / TransTEE
β32Updated 2 years ago
Alternatives and similar repositories for TransTEE:
Users that are interested in TransTEE are comparing it to the libraries listed below
- ππ Dose response networks (DRNets) are a method for learning to estimate individual dose-response curves for multiple parametric treatmβ¦β87Updated last year
- β58Updated 3 years ago
- Code for TEDVAE, a VAE-based treatment effect estimation algorithm.β23Updated 2 years ago
- Causal inference is a critical task in various fields such as healthcare,economics, marketing and education. Recently, there have beensigβ¦β21Updated 3 years ago
- Codebase for GANITE: Estimation of Individualized Treatment Effects using GANs - ICLR 2018β58Updated 4 years ago
- Data derived from the Linked Births and Deaths Data (LBIDD); simulated pairs of treatment assignment and outcomes; scoring codeβ82Updated 6 years ago
- β37Updated 6 years ago
- Causal Effect Inference for Structured Treatments (SIN) (NeurIPS 2021)β42Updated 2 years ago
- Code for the WSDM '20 paper, Learning Individual Causal Effects from Networked Observational Data.β74Updated 3 years ago
- Sklearn-style implementations of Neural Network-based Conditional Average Treatment Effect (CATE) Estimators.β136Updated 9 months ago
- Code for NeurIPS 2020 paper: "Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks" by I. Bicaβ¦β29Updated 4 years ago
- β27Updated 2 years ago
- ββ Perfect Match is a simple method for learning representations for counterfactual inference with neural networks.β126Updated last year
- Causal Inferenceβ10Updated 4 years ago
- Counterfactual Regressionβ23Updated 8 years ago
- β44Updated 6 years ago
- [ICLR 2021] Code for: Varying Coefficient Neural Network with Functional Targeted Regularization for Estimating Continuous Treatment Effeβ¦β73Updated 2 years ago
- Reproducing Shalit et al.'s Individual Treatment Effect model. This is a deep neural net that can be applied to various problems in causaβ¦β17Updated 2 years ago
- Implementation of Johansson, Fredrik D., Shalit, Uri, and Sontag, David. Learning representations for counterfactual inference - ICML, 20β¦β12Updated 4 years ago
- BITES: Balanced Individual Treatment Effect for Survival dataβ18Updated last year
- Code for paper "Estimating Causal Effects on Networked Observational Data via Representation Learning"β15Updated last year
- Code for "Learning End-to-End Patient Representations through Self-Supervised Covariate Balancing for Causal Treatment Effect Estimation"β22Updated last year
- Code for ICML 2020 paper: "Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders" by I. β¦β52Updated 4 years ago
- A curated list of awesome work on causal inference, particularly in machine learning.β100Updated 3 years ago
- Software and pre-processed data for "Using Embeddings to Correct for Unobserved Confounding in Networks"β56Updated 2 years ago
- Realistic benchmark for different causal inference methods. The realism comes from fitting generative models to data with an assumed causβ¦β72Updated 3 years ago
- β23Updated 3 years ago
- Non-parametrics for Causal Inferenceβ43Updated 3 years ago
- LEAP is a tool for discovering latent temporal causal relations with gradient-based neural network.β35Updated 2 years ago
- Reimplementation of NOTEARS in Tensorflowβ35Updated last year