juangamella / icpLinks
Python implementation of the Invariant Causal Prediction (ICP) algorithm, from the 2015 paper "Causal inference using invariant prediction: identification and confidence intervals" by Jonas Peters, Peter Bühlmann and Nicolai Meinshausen.
☆24Updated last year
Alternatives and similar repositories for icp
Users that are interested in icp are comparing it to the libraries listed below
Sorting:
- This repository captures source code and data sets for our paper at the Causal Discovery & Causality-Inspired Machine Learning Workshop a…☆62Updated last year
- Causal Discovery from Nonstationary/Heterogeneous Data.☆54Updated 5 years ago
- ☆40Updated 6 years ago
- Code for the Structural Agnostic Model (https://arxiv.org/abs/1803.04929)☆54Updated 4 years ago
- An open-source package of causal feature selection and causal (Bayesian network) structure learning (C/C++ version)☆62Updated 4 years ago
- ☆29Updated last year
- 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
- pyCausalFS:A Python Library of Causality-based Feature Selection for Causal Structure Learning and Classification☆70Updated 4 years ago
- ☆32Updated 7 years ago
- Repository for "Differentiable Causal Discovery from Interventional Data"☆76Updated 3 years ago
- Code for paper: NTS-NOTEARS: Learning Nonparametric DBNs With Prior Knowledge☆27Updated 2 years ago
- Python code for NeurIPS 2018 paper "Causal Inference and Mechanism Clustering of A Mixture of Additive Noise Models"☆23Updated 6 years ago
- Python package for the creation, manipulation, and learning of Causal DAGs☆155Updated 2 years ago
- Code for a variety of nonlinear conditional independence tests and 'nonlinear Invariant Causal Prediction' to estimate the causal parents…☆17Updated 5 years ago
- TIme series DiscoverY BENCHmark (tidybench)☆38Updated last year
- On the Role of Sparsity and DAG Constraints for Learning Linear DAGs☆33Updated 4 years ago
- Statistical Recurrent Unit based time series generative models for detecting nonlinear Granger causality☆36Updated 5 years ago
- ☆97Updated 2 years ago
- Implementation of the MIWAE method for deep generative modelling of incomplete data sets.☆41Updated last year
- Method based on neural networks and variational inference for causal discovery under latent interventions, i. e. learning a shared causal…☆19Updated 3 years ago
- Code for `BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery`, Neurips 2021☆26Updated 4 years ago
- Replication code for the article "Learning Functional Causal Models with Generative Neural Networks"☆100Updated 6 years ago
- Code for ICLR 2020 paper: "Estimating counterfactual treatment outcomes over time through adversarially balanced representations" by I. B…☆62Updated last year
- Implementation of "DAGs with NO TEARS: Smooth Optimization for Structure Learning", by Zheng et al. (2018)☆50Updated 6 years ago
- Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.☆32Updated 6 years ago
- A generalized score-based method for Causal Discovery☆17Updated 4 years ago
- Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations (ICML 2022)☆26Updated 3 years ago
- A Pytorch implementation of missing data imputation using optimal transport.☆104Updated 4 years ago
- Adaptive and Reliable Classification: efficient conformity scores for multi-class classification problems☆33Updated 2 years ago
- Classifier based mutual information, conditional mutual information estimation; conditional independence testing☆35Updated 6 years ago