maxwshen / iap-cidl
Causal Inference & Deep Learning, MIT IAP 2018
☆85Updated 7 years ago
Alternatives and similar repositories for iap-cidl:
Users that are interested in iap-cidl are comparing it to the libraries listed below
- ☆87Updated 4 years ago
- Code for the Structural Agnostic Model (https://arxiv.org/abs/1803.04929)☆52Updated 4 years ago
- Code for "Neural causal learning from unknown interventions"☆99Updated 4 years ago
- Uncertainty in Conditional Average Treatment Effect Estimation☆30Updated 4 years ago
- Replication code for the article "Learning Functional Causal Models with Generative Neural Networks"☆99Updated 5 years ago
- Code for "Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models" (ICML 2019)☆42Updated 4 years ago
- General Latent Feature Modeling for Heterogeneous data☆48Updated 11 months ago
- NeurIPS 2017 best paper. An interpretable linear-time kernel goodness-of-fit test.☆67Updated 5 years ago
- Code for "A Meta Transfer Objective For Learning To Disentangle Causal Mechanisms"☆126Updated 6 years ago
- Inference and Representation (DS-GA-1005, CSCI-GA.2569), fall 18☆65Updated 6 years ago
- Deep Markov Models☆130Updated 5 years ago
- Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019☆41Updated 2 years ago
- OxCSML research group reading groups and meetings at the Department of Statistics, University of Oxford.☆93Updated 3 years ago
- Variational Autoencoders with Gaussian Mixture Latent Space☆36Updated 7 years ago
- References at the Intersection of Causality and Reinforcement Learning☆89Updated 4 years ago
- Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)☆65Updated 5 years ago
- Contains all materials for the paper "A counterfactual simulation model of causal judgment".☆23Updated 3 years ago
- Gaussian Processes in Pytorch☆75Updated 5 years ago
- Code for "Towards a learning theory of cause-effect inference" (ICML 2015).☆29Updated 4 years ago
- python code for kernel methods☆38Updated 6 years ago
- ☆29Updated 6 years ago
- ☆141Updated 7 years ago
- Causal Effect Inference with Deep Latent-Variable Models☆334Updated 4 years ago
- Edward content including papers, posters, and talks☆91Updated 4 years ago
- Exercises for the Tutorial on Approximate Bayesian Inference at the Data Science Summer School 2018☆22Updated 6 years ago
- Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)☆82Updated 4 years ago
- Source code for Naesseth et. al. "Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms" (2017)☆39Updated 7 years ago
- Kaggle's Causality Challenge Solution for team FirfiD☆26Updated 11 years ago
- Code for paper EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE☆38Updated last year
- Deep Generative Models with Stick-Breaking Priors☆95Updated 8 years ago