d909b / cxplainLinks
Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.
β132Updated 5 years ago
Alternatives and similar repositories for cxplain
Users that are interested in cxplain are comparing it to the libraries listed below
Sorting:
- Repository for Deep Structural Causal Models for Tractable Counterfactual Inferenceβ291Updated 2 years ago
- Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" π§ (ICLR 2019)β129Updated 4 years ago
- β125Updated 4 years ago
- Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" htβ¦β128Updated 4 years ago
- Code for ICLR 2020 paper: "Estimating counterfactual treatment outcomes over time through adversarially balanced representations" by I. Bβ¦β66Updated last year
- Codebase for INVASE: Instance-wise Variable Selection - 2019 ICLRβ64Updated 5 years ago
- Code for "Neural causal learning from unknown interventions"β104Updated 5 years ago
- Code for our ICML '19 paper: Neural Network Attributions: A Causal Perspective.β51Updated 4 years ago
- A lightweight implementation of removal-based explanations for ML models.β59Updated 4 years ago
- Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.β32Updated 6 years ago
- Tools for training explainable models using attribution priors.β125Updated 4 years ago
- Neural Additive Models (Google Research)β73Updated 4 years ago
- Calibration library and code for the paper: Verified Uncertainty Calibration. Ananya Kumar, Percy Liang, Tengyu Ma. NeurIPS 2019 (Spotligβ¦β151Updated 3 years ago
- β91Updated 2 years ago
- Implementation of the paper "Shapley Explanation Networks"β88Updated 4 years ago
- Code for "Generative causal explanations of black-box classifiers"β35Updated 4 years ago
- ββ Perfect Match is a simple method for learning representations for counterfactual inference with neural networks.β131Updated 2 years ago
- Causal Inference & Deep Learning, MIT IAP 2018β89Updated 7 years ago
- Deep Neural Decision Treesβ164Updated 3 years ago
- Code for the Structural Agnostic Model (https://arxiv.org/abs/1803.04929)β54Updated 4 years ago
- β30Updated 7 years ago
- An implementation of the Deep Neural Decision Forests in PyTorchβ165Updated 6 years ago
- Codebase for "Deep Learning for Case-based Reasoning through Prototypes: A Neural Network that Explains Its Predictions" (to appear in AAβ¦β76Updated 8 years ago
- β40Updated 6 years ago
- β32Updated 7 years ago
- Realistic benchmark for different causal inference methods. The realism comes from fitting generative models to data with an assumed causβ¦β83Updated 4 years ago
- Code repository for our paper "Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift": https://arxiv.org/abs/1810.119β¦β107Updated last year
- Keras implementation for DASP: Deep Approximate Shapley Propagation (ICML 2019)β62Updated 6 years ago
- Official codebase for "Distribution-Free, Risk-Controlling Prediction Sets"β87Updated last year
- Wrapper for a PyTorch classifier which allows it to output prediction sets. The sets are theoretically guaranteed to contain the true claβ¦β253Updated 2 years ago