paras2612 / CauseBox
Causal inference is a critical task in various fields such as healthcare,economics, marketing and education. Recently, there have beensignificant advances through the application of machine learningtechniques, especially deep neural networks. Unfortunately, to-datemany of the proposed methods are evaluated on different (data,software/hardware, h…
☆21Updated 3 years ago
Alternatives and similar repositories for CauseBox:
Users that are interested in CauseBox are comparing it to the libraries listed below
- Code for the WSDM '20 paper, Learning Individual Causal Effects from Networked Observational Data.☆74Updated 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
- Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations (ICML 2022)☆27Updated 2 years ago
- LEAP is a tool for discovering latent temporal causal relations with gradient-based neural network.☆35Updated 2 years ago
- Code for TEDVAE, a VAE-based treatment effect estimation algorithm.☆24Updated 2 years ago
- ☆27Updated 2 years ago
- Code for paper "Estimating Causal Effects on Networked Observational Data via Representation Learning"☆15Updated last year
- ☆33Updated 2 years ago
- ☆44Updated 6 years ago
- ➕➕ Perfect Match is a simple method for learning representations for counterfactual inference with neural networks.☆126Updated 2 years ago
- On the Role of Sparsity and DAG Constraints for Learning Linear DAGs☆33Updated 3 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
- ☆92Updated 2 years ago
- Code for ICLR 2020 paper: "Estimating counterfactual treatment outcomes over time through adversarially balanced representations" by I. B…☆58Updated last year
- Codebase for GANITE: Estimation of Individualized Treatment Effects using GANs - ICLR 2018☆59Updated 4 years ago
- Implementation of "DAGs with NO TEARS: Smooth Optimization for Structure Learning", by Zheng et al. (2018)☆50Updated 6 years ago
- Software and pre-processed data for "Using Embeddings to Correct for Unobserved Confounding in Networks"☆56Updated 2 years ago
- ☆43Updated last year
- ☆59Updated 3 years ago
- Code for Estimating Multi-cause Treatment Effects via Single-cause Perturbation (NeurIPS 2021)☆12Updated 3 years ago
- Repository for "Differentiable Causal Discovery from Interventional Data"☆73Updated 3 years ago
- Causal Effect Inference for Structured Treatments (SIN) (NeurIPS 2021)☆42Updated 2 years ago
- ☆37Updated 6 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
- Code for "Counterfactual Fairness" (NIPS2017)☆52Updated 6 years ago
- Datasets for Causal-Structure-Learning Repo☆15Updated 4 years ago
- An open-source package of causal feature selection and causal (Bayesian network) structure learning (C/C++ version)☆59Updated 4 years ago
- Code for NeurIPS 2020 paper: "Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks" by I. Bica…☆29Updated 4 years ago
- Data derived from the Linked Births and Deaths Data (LBIDD); simulated pairs of treatment assignment and outcomes; scoring code☆82Updated 6 years ago
- Causal Discovery from Nonstationary/Heterogeneous Data.☆53Updated 4 years ago