CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms

Repository for Deep Structural Causal Models for Tractable Counterfactual Inference

Code for "Counterfactual Token Generation in Large Language Models", Arxiv 2024.

Counterfactual SHAP: a framework for counterfactual feature importance

A long-form article and practical framework for designing machine learning systems that warn instead of decide. Covers regimes vs decimals, levers over labels, reversible alerts, anti-coercion UI patterns, auditability, and the “Warning Card” template, so ML preserves human agency while staying useful under uncertainty.

Why And What If: Causal Inference for Everyone

Similarity-first interpretability studio for breast tumor samples: pick a case, find its closest “twins” (benign/malignant look-alikes), visualize neighborhood structure, compare feature fingerprints, and run minimal-change counterfactual edits toward a target class. Educational demo only, not for diagnosis.

Code for "Counterfactual Explanations in Sequential Decision Making Under Uncertainty", NeurIPS 2021

Repo of the paper "On the Robustness of Sparse Counterfactual Explanations to Adverse Perturbations"

A hyperintensional theorem prover for rapidly prototyping modular semantic theories

cfid: R package for identifying counterfactuals.

Counterfactual Shapley Additive Explanation: Experiments

Research project on generation of counterfactuals for eXplainable AI, based on Bayesian Generation

Source code for the BlackBoxNLP 2024 @ EMNLP paper "Enhancing adversarial robustness in Natural Language Inference using explanations"

Controllable Sequence Editing for Counterfactual Generation

Code for "Finding Counterfactually Optimal Action Sequences in Continuous State Spaces", NeurIPS 2023.

Causal modelling with FitBit and Gluroo data for blood-glucose time-series evolution

A baseline genetic algorithm for the discovery of counterfactuals, implemented in Python for ease of use and heavily leveraging NumPy for speed.

Semantic Meaningfulness: Evaluating counterfactual approaches for real world plausibility

counterfactual explanations for ML hyperparameters