rik-helwegen / CEVAE_pytorch
☆44Updated 6 years ago
Alternatives and similar repositories for CEVAE_pytorch:
Users that are interested in CEVAE_pytorch are comparing it to the libraries listed below
- ☆59Updated 3 years ago
- Codebase for GANITE: Estimation of Individualized Treatment Effects using GANs - ICLR 2018☆59Updated 4 years ago
- Causal Inference☆10Updated 4 years ago
- ☆92Updated 2 years ago
- Code for TEDVAE, a VAE-based treatment effect estimation algorithm.☆24Updated 2 years ago
- ➕➕ Perfect Match is a simple method for learning representations for counterfactual inference with neural networks.☆126Updated 2 years ago
- Code for Estimating Multi-cause Treatment Effects via Single-cause Perturbation (NeurIPS 2021)☆12Updated 3 years ago
- 💉📈 Dose response networks (DRNets) are a method for learning to estimate individual dose-response curves for multiple parametric treatm…☆87Updated 2 years ago
- Implementation of "DAGs with NO TEARS: Smooth Optimization for Structure Learning", by Zheng et al. (2018)☆50Updated 6 years ago
- Disentangled gEnerative cAusal Representation (DEAR)☆60Updated 2 years ago
- Code for the WSDM '20 paper, Learning Individual Causal Effects from Networked Observational Data.☆74Updated 3 years ago
- ☆33Updated 2 years ago
- ☆23Updated 3 years ago
- On the Role of Sparsity and DAG Constraints for Learning Linear DAGs☆33Updated 3 years ago
- Counterfactual Regression☆23Updated 8 years ago
- Causal inference is a critical task in various fields such as healthcare,economics, marketing and education. Recently, there have beensig…☆21Updated 3 years ago
- Causal Discovery from Nonstationary/Heterogeneous Data.☆53Updated 4 years ago
- Code for ICLR 2020 paper: "Estimating counterfactual treatment outcomes over time through adversarially balanced representations" by I. B…☆58Updated last year
- BITES: Balanced Individual Treatment Effect for Survival data☆18Updated last year
- ☆19Updated 3 years ago
- Neural Causal Model (NCM) implementation by the authors of The Causal Neural Connection.☆25Updated 2 years ago
- LEAP is a tool for discovering latent temporal causal relations with gradient-based neural network.☆35Updated 2 years ago
- Code for "Learning End-to-End Patient Representations through Self-Supervised Covariate Balancing for Causal Treatment Effect Estimation"☆22Updated last year
- ☆27Updated 2 years ago
- [ICLR 2021] Code for: Varying Coefficient Neural Network with Functional Targeted Regularization for Estimating Continuous Treatment Effe…☆75Updated 2 years ago
- Code for `BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery`, Neurips 2021☆26Updated 3 years ago
- ☆305Updated 3 years ago
- Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.☆30Updated 5 years ago
- ☆37Updated 6 years ago
- Repository for "Differentiable Causal Discovery from Interventional Data"☆73Updated 3 years ago