Alternatives and similar repositories for awesome-counterfactual-explanations

Users that are interested in awesome-counterfactual-explanations are comparing it to the libraries listed below

• weitianxin / awesome-distribution-shift
  A curated list of papers and resources about the distribution shift in machine learning.
  ☆123Updated 2 years ago
• facebookresearch / CausalRepID
  Experiments to reproduce results in Interventional Causal Representation Learning.
  ☆26Updated 2 years ago
• MaheepChaudhary / Causality-in-Trustworthy-Machine-Learning
  The repository contains lists of papers on causality and how relevant techniques are being used to further enhance deep learning era comp…
  ☆98Updated 2 years ago
• namkoong-lab / whyshift
  A python package providing a benchmark with various specified distribution shift patterns.
  ☆58Updated last year
• changdaeoh / FarconVAE
  Official implementation for KDD'22 paper "Learning Fair Representation via Distributional Contrastive Disentanglement"
  ☆23Updated 3 years ago
• opendataval / opendataval
  OpenDataVal: a Unified Benchmark for Data Valuation in Python (NeurIPS 2023)
  ☆99Updated 8 months ago
• iancovert / shapley-regression
  For calculating Shapley values via linear regression.
  ☆71Updated 4 years ago
• BaeHeeSun / NPC
  ☆25Updated last year
• phlippe / ENCO
  Official repository of the paper "Efficient Neural Causal Discovery without Acyclicity Constraints"
  ☆86Updated last year
• xu-ji / information-bottleneck
  Deep Learning & Information Bottleneck
  ☆61Updated 2 years ago
• edebrouwer / cfqp
  Deep Counterfactual Prediction with Categorical Backward Variables
  ☆12Updated 2 years ago
• ykwon0407 / fast_dist_shapley
  Efficient Computation and Analysis of Distributional Shapley Values (AISTATS 2021)
  ☆22Updated 2 years ago
• archon159 / SELOR
  ☆40Updated 2 years ago
• MangoKiller / SimOAR_OAR
  ☆10Updated last year
• hsouri / BayesianTransferLearning
  ☆109Updated 3 years ago
• dh7401 / T-Cal
  ☆10Updated 3 years ago
• facebookresearch / nbm-spam
  Training and evaluating NBM and SPAM for interpretable machine learning.
  ☆78Updated 2 years ago
• patrickrchao / DiffusionBasedCausalModels
  ☆27Updated 6 months ago
• monk1337 / Awesome-Robust-Machine-Learning
  A curated list of Robust Machine Learning papers/articles and recent advancements.
  ☆33Updated 3 years ago
• hans66hsu / GATS
  Source code of "What Makes Graph Neural Networks Miscalibrated?" (NeurIPS 2022)
  ☆24Updated 4 months ago
• pfnet-research / TabCSDI
  A code for the NeurIPS 2022 Table Representation Learning Workshop paper: "Diffusion models for missing value imputation in tabular data"
  ☆56Updated last year
• vios-s / DiffAN
  Diffusion Models for Causal Discovery
  ☆87Updated 2 years ago
• UW-Madison-Lee-Lab / LanguageInterfacedFineTuning
  Code for Language-Interfaced FineTuning for Non-Language Machine Learning Tasks.
  ☆132Updated 11 months ago
• yandex-research / structural-graph-shifts
  New structural distributional shifts for evaluating graph models
  ☆15Updated 2 years ago
• google-deepmind / distribution_shift_framework
  This repository contains the code of the distribution shift framework presented in A Fine-Grained Analysis on Distribution Shift (Wiles e…
  ☆84Updated this week
• yandex-research / tabgraphs
  A benchmark of meaningful graph datasets with tabular node features
  ☆14Updated this week
• LFhase / PAIR
  [ICLR 2023, ICLR DG oral] PAIR, the optimizer and model selection criteria for OOD Generalization
  ☆53Updated last year
• psanch21 / causal-flows
  ☆51Updated last year
• jaehyun513 / STUNT
  Official PyTorch implementation of STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled Tables (ICLR 2023 Spotlight)…
  ☆59Updated 2 years ago
• namratadeka / circe
  Efficient Conditionally Invariant Representation Learning (ICLR 2023, Oral)
  ☆21Updated 2 years ago