ABC method analyzes diffusion model dependence without retraining.
problem Difficulties in characterizing diffusion models' dependence on training data.
method Ablation-based counterfactuals using model components trained on different splits.
result Demonstrates limits of training data attribution and existence of unattributable samples.
CLUE method interprets uncertainty from BNNs by showing how inputs change to increase confidence.
problem Lack of work on interpreting uncertainty estimates from probabilistic models.
method CLUE method uses counterfactual explanations to interpret uncertainty from BNNs.
result CLUE outperforms baselines and helps practitioners understand predictive uncertainty.
Dual behavior policy improves reinforcement learning across various environments.
problem Challenges in reinforcement learning with inconsistent or unknown environments.
method Dual, advantage-based behavior policy using counterfactual regret minimization.
result Demonstrated improved performance over baseline models in diverse environments.
Model identifies order splitting and liquidity replenishment as necessary for the square-root law of market impact.
problem Quantifying the square-root law of market impact and identifying its underlying mechanisms.
method Minimal limit-order-book model with heterogeneous interacting agents calibrated against real data. Counterfactual ablation to isolate mechanisms.
result Order splitting and liquidity replenishment are necessary for the square-root law of market impact.
Causal normalizing flows recover causal models from observational data.
problem Recovering causal models from observational data.
method Use autoregressive normalizing flows and analyze design choices.
result Causal normalizing flows can capture causal data-generating processes.
Paper addresses theoretical risks in neural MCCFR, proposing Robust Deep MCCFR for improved performance.
problem Theoretical risks in neural MCCFR, especially in large games.
method Adaptive framework with selective component deployment, including target networks, exploration, and variance-aware training.
result Robust Deep MCCFR achieves significant exploitability improvements in both Kuhn and Leduc Poker.
Method generates anatomically-controllable medical images with segmentation guidance.
problem Challenging to enforce anatomical constraints in generated medical images.
method Segmentation-guided diffusion models with random mask ablation training.
result New state-of-the-art in faithfulness to input anatomical masks.
Study ablated data augmentation techniques and their mathematical equivalence to penalties.
problem Lack of mathematical understanding of differences between ablated data augmentation techniques.
method Formal model of mean ablated data augmentation and inverted dropout for linear regression; empirical validation for deep networks.
result Ablated data augmentation and inverted dropout are mathematically equivalent to penalties in optimization.
R package for counterfactual explanation methods.
problem Lack of unified interfaces for counterfactual explanation methods.
method Developed a modular R6-based interface for three existing counterfactual methods and proposed extensions.
result Comparison of implemented methods' quality and runtime behavior.
Ablation studies show BCF model's propensity score is not essential for treatment effect estimation.
problem Understanding the necessity of propensity score in nonparametric treatment effect estimation.
method Partial ablation studies of Bayesian Causal Forest (BCF) model.
result Excluding estimated propensity score does not affect treatment effect estimation or uncertainty quantification.
Post-hoc explanations of machine learning models are crucial for people to understand and act on algorithmic predictions. An intriguing class of explanations is through counterfactuals, hypothetical examples that show people how to obtain a different prediction. We posit that effective counterfactual explanations shoul…
DeDUCE efficiently generates realistic counterfactuals for image classifiers.
problem Generating accurate counterfactual explanations for large image classifiers.
method Developed a new algorithm using spectral normalization to efficiently generate counterfactuals.
result Our algorithm consistently produces counterfactuals closer to the original inputs with comparable realism.
We establish a foundation for multivariate counterfactual identification using dynamic optimal transport.
problem Addressing the open question of counterfactual identification for high-dimensional multivariate outcomes from observational data.
method Establish a foundation for multivariate counterfactual identification using continuous-time flows, including non-Markovian settings, with tools from dynamic optimal transport.
result Characterise the conditions under which flow matching yields a unique, monotone, and rank-preserving counterfactual transport map, ensuring consistent inference.
A new framework for robust and coherent counterfactual transports.
problem Estimating joint distributions over counterfactual outcomes in personalized decision-making and treatment risk assessment.
method Counterfactual cocycles that use algebraic structure to provide coherence and identifiability guarantees, bridging the gap between bijective SCMs and OT methods.
result Counterfactual cocycles provide state-of-the-art performance and noise-robustness across synthetic benchmarks and a real-world study.
New approach uses 'forward-looking' counterfactuals for treatment choice.
problem Using traditional 'retrospective' counterfactuals in treatment choice leads to counterintuitive results.
method Introduces 'counterfactual treatment choice' for forward-looking counterfactuals.
result Mismatches between interventional and forward-looking counterfactuals can lead to counterintuitive results.
The paper analyzes counterfactual invariance and its relation to conditional independence.
problem Understanding the relationship between counterfactual invariance and conditional independence.
method Theoretical analysis of existing definitions, graphical implications, and mathematical proofs.
result Counterfactual invariance implies conditional independence, but not the other way around.
New framework improves counterfactual predictions using causal inference.
problem Challenges in predicting counterfactual outcomes with limited covariates and high-dimensional outcomes.
method Variational Bayesian causal inference framework for counterfactual generative modeling.
result Framework encourages disentangled exogenous noise and correct identification of causal effects.
Combines structured inference and targeted learning to tackle causal inference challenges.
problem Treatment assignment heterogeneity and lack of counterfactual data.
method Factorizes joint distribution into risk, confounding, instrumental, and miscellaneous factors; applies regularizer derived from influence curve.
result TVAE demonstrates competitive and state-of-the-art performance on benchmark datasets.
Counterfactual data augmentations may not ensure OOD robustness if performed by a context-guessing machine.
problem Deep learning models lack out-of-distribution robustness due to reliance on spurious features.
method Theoretical analysis and demonstration of counterfactual data augmentations performed by a context-guessing machine.
result Counterfactual data augmentations by a context-guessing machine do not lead to robust OOD classifiers.
New method improves model reconstruction using counterfactuals and polytope theory.
problem Reconstructing models with minimal input changes and avoiding decision boundary shifts.
method Using polytope theory to derive loss functions that treat counterfactuals differently from ordinary instances.
result Improves fidelity between target and surrogate model predictions on multiple datasets.
New method debiases counterfactual distributions using observational data.
problem Estimating counterfactual distributions under interventions without relying on observational data.
method Flow-matching approach to learn counterfactual distributions from observational data.
result Deconfounding flows outperform existing debiased counterfactual distribution estimators.
DeepBC method computes backtracking counterfactuals in deep causal models.
problem Computing valid counterfactuals in complex causal models.
method DeepBC method using Langevin Monte Carlo or constrained optimization.
result DeepBC provides causally compliant, versatile, and modular counterfactuals.
Proposes a method to generate counterfactuals for ensemble models using entropic risk measures.
problem Finding a single counterfactual explanation for an ensemble of models.
method Incorporates entropic risk measure into a constrained optimization to generate counterfactuals valid for an adjustable fraction of models.
result Entropic risk measure allows generation of counterfactuals valid for all models in the ensemble under a limiting case.
We define and compute plausible counterfactual explanations using density constraints.
problem Efficiently compute plausible counterfactual explanations for machine learning models.
method Propose and study a formal definition of plausible counterfactual explanations, use density estimators, and introduce convex density constraints.
result Convex density constraints ensure plausible and feasible counterfactual explanations.
Counterfactual explanations can be obtained by identifying the smallest change made to a feature vector to qualitatively influence a prediction; for example, from 'loan rejected' to 'awarded' or from 'high risk of cardiovascular disease' to 'low risk'. Previous approaches often emphasized that counterfactuals should be…
New method warns of counterfactual non-identifiability in DSCMs.
problem Counterfactual inference from observational data is non-identifiable even without unobserved confounding.
method Prove counterfactual identifiability for monotonic generation mechanisms, provide impossibility result for general mechanisms, propose method for estimating worst-case errors.
result Non-identifiability of counterfactual inference from observational data, even in absence of unobserved confounding.
Develops methods for finding counterfactual explanations in sequential decision making.
problem Finding counterfactual explanations for sequential decision making processes.
method Formal characterization of sequential actions and states using Markov decision processes and Gumbel-Max structural causal model. Introduces a polynomial time algorithm based on dynamic programming.
result Algorithm finds optimal counterfactual explanations for sequential decision making.
New methods create counterfactuals for image regression models.
problem Creating interpretable explanations for regression models in images.
method Two methods using diffusion-based generative models to create counterfactuals.
result Diffusion-based methods produce realistic, semantic, and smooth counterfactuals.
Algorithm simulates counterfactuals for fairness analysis.
problem Analytical intractability of counterfactuals in conditional distributions.
method Proposes an algorithm using particle filtering for discrete and continuous variables.
result Asymptotically valid inference for counterfactuals.
In this paper, we study counterfactual fairness in text classification, which asks the question: How would the prediction change if the sensitive attribute referenced in the example were different? Toxicity classifiers demonstrate a counterfactual fairness issue by predicting that "Some people are gay" is toxic while "…
This study analyzes counterfactual explanations for student success models.
problem Improving trust in machine learning models for student success prediction.
method Comparison of counterfactual generation methods (WhatIf, Multi-Objective, Nearest Instance) for student success prediction models.
result WhatIf Counterfactual Explanations are more effective for student success prediction models.
New approach to counterfactual reasoning avoids demographic interventions.
problem Limitations of traditional counterfactual reasoning in AI systems.
method Backtracking counterfactual approach instead of interventional.
result Allows addressing social concerns without demographic interventions.
Counterfactual fairness not equivalent to demographic parity, finds study.
problem Equivalence between causal and probabilistic concepts in fairness metrics.
method Close examination of recent claim about counterfactual fairness.
result Counterfactual fairness is not equivalent to demographic parity.
New approach to counterfactual reasoning in AI and psychology.
problem Challenges to conventional counterfactual reasoning in AI and psychology.
method Formalizes a backtracking approach to counterfactuals within the SCM framework.
result First general account and algorithmisation of backtracking counterfactuals.
CVIB uses information theory to learn counterfactuals from MNAR data without RCTs.
problem Debiasing learning from missing-not-at-random (MNAR) data in recommendation systems.
method CVIB, a variational information bottleneck, separates task-aware mutual information into factual and counterfactual parts.
result CVIB significantly enhances both shallow and deep models in recommendation systems.
Counterfactuals help compare different scenarios in CI and XAI.
problem Comparing how counterfactuals are used in causal inference and explainable AI.
method Formal definition and comparison of counterfactuals in CI and XAI.
result Identified opportunities for cross-fertilization between CI and XAI.
The paper connects counterfactual fairness to robust prediction and group fairness using causal context.
problem The challenge of ensuring fairness in AI systems when counterfactuals cannot be directly observed.
method Using causal context to bridge counterfactual fairness, robust prediction, and group fairness.
result Counterfactual fairness is equivalent to group fairness metrics in specific contexts.
ARFs generate plausible counterfactuals for models, improving model understanding.
problem Creating realistic counterfactuals for model analysis.
method Adversarial Random Forests (ARFs) for generating plausible counterfactuals.
result ARFs efficiently generate plausible counterfactuals in a model-agnostic way.
Study uses artificial counterfactuals to show lockdowns reduced US case and death counts.
problem Impact of lockdowns on US case and death counts during the pandemic.
method Artificial counterfactual approach comparing states with and without lockdowns.
result Average two times more cases would have occurred without lockdowns.
EXOC framework uses auxiliary variables for counterfactual fairness in machine learning.
problem Balancing fairness and predictive accuracy in models with sensitive attributes.
method EXOC framework uses auxiliary variables to define an auxiliary node and a control node for counterfactual fairness.
result EXOC framework outperforms state-of-the-art approaches in achieving counterfactual fairness.
Study counterfactuals in cyclic systems with shifts and scales.
problem Counterfactual inference in cyclic systems with shifts and scales.
method Shift-scale interventions in cyclic SCMs.
result Valid inference in cyclic systems with shifts and scales.
This paper introduces collective counterfactual explanations for groups of instances in classification models.
problem Understanding how classification models make decisions for groups of instances.
method Novel Mathematical Optimization models to find collective counterfactual explanations that minimize total perturbation cost.
result Detects critical features for entire dataset classification and handles outliers.
Paper proposes a modified fairness constraint to address shortcomings of counterfactual fairness.
problem Counterfactual fairness is not a necessary condition for algorithmic fairness.
method Analyzed hypothetical scenario and explicated discrimination to develop causal relevance fairness.
result Causal relevance fairness is a modified constraint that circumvents shortcomings of counterfactual fairness.
New diffusion models improve counterfactual image generation with semantic control.
problem Challenges in preserving identity, maintaining quality, and ensuring causal model faithfulness in counterfactual image generation.
method Integrates semantic representations into diffusion models through Pearlian causality, introducing spatial, semantic, and dynamic abduction.
result Demonstrates high-level semantic identity preservation and principled trade-offs between faithful causal control and identity preservation.
Proposes MOC method for better counterfactual explanations in ML models.
problem Difficulties in balancing multiple objectives for counterfactual explanations.
method Translates counterfactual search into a multi-objective optimization problem.
result Returns diverse counterfactuals with different trade-offs and maintains feature diversity.
Proposes Exogenous Matching for efficient counterfactual estimation.
problem Efficient estimation of counterfactual expressions in general settings.
method Transforms variance minimization into conditional distribution learning.
result Outperforms other importance sampling methods in counterfactual estimation.
Local MDI+ improves feature importance for tree-based models, enhancing interpretability and performance.
problem Lack of instance-specific feature importance for tree-based models.
method Local MDI+ extends MDI+ framework to provide instance-specific feature importances.
result Local MDI+ outperforms existing baselines, improving predictive performance by 10%.
The paper tackles spurious correlations in machine learning models and introduces counterfactual invariance.
problem Spurious correlations in machine learning models that depend on irrelevant parts of input data.
method The paper uses causal inference to stress test models and introduces counterfactual invariance as a formal requirement.
result Counterfactual invariance is a requirement for models to be robust to irrelevant perturbations in input data.