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.
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 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.
Flow IV uses IVs to infer counterfactuals in complex models.
problem Identifying causal effects and counterfactual reasoning in nonseparable outcome models.
method Utilizes instrumental variables and normalizing flows to estimate and infer counterfactual outcomes.
result Identifies a method to make causal inferences from observed data in nonseparable models.
Self-Distilled Disentanglement improves counterfactual predictions by separating variables.
problem Improving counterfactual predictions in the presence of confounders and unobserved variables.
method Self-Distilled Disentanglement framework based on information theory.
result Effective counterfactual inference in synthetic and real-world datasets.
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.
Generates counterfactuals in target domain from source domain observations.
problem Cross-domain learning with domain shifts and lack of parallel datasets.
method Unsupervised, Neural Causal Models, Joint Causal Graphs, Effect-Intrinsic vs Domain-Intrinsic Variables.
result Framework generates counterfactuals that closely match ground truth.
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.
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.
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.
Introduces info intervention to handle causal questions and check counterfactual variables.
problem Controversial interpretation of causal questions for non-manipulable variables and lack of power to check counterfactual variables.
method Intervenes input/output information of causal mechanisms, providing causal diagrams for communication and theoretical focus.
result Causal diagrams based on info intervention provide a new perspective on information transfer as causality.
Model predicts counterfactuals under domain shift and inaccessible variables.
problem Runtime domain corruption impairs counterfactual prediction.
method Subsumes counterfactual prediction under domain adaptation, uses adversarial domain adaptation to reduce distribution disparity.
result VEGAN outperforms baselines in individual-level treatment effect estimation.
Paper proposes a method to estimate counterfactual outcomes without a known SCM.
problem Estimating counterfactual outcomes without a known structural causal model.
method Introduces rank preservation assumption and a novel ideal loss for unbiased learning of counterfactual outcomes.
result The proposed method is effective and unbiased, as shown by theoretical analysis and experiments.
New algorithm makes machine learning fairer by removing bias from data.
problem Reduces bias in machine learning models through orthogonal data transformation.
method Orthogonal to Bias (OB) algorithm based on structural causal models.
result Promotes counterfactual fairness without sacrificing model accuracy.
New method for fair regression using optimal transport.
problem Learning fair regression models under counterfactual fairness constraints.
method Causal uncertainty view, optimal transport, post-processing method.
result High-probability fairness guarantees with O(n−1/3) decay. Quantum oracles help identify counterfactuals better than classical ones.
problem Identifying unknown causal parameters in causal models.
method Using quantum oracles to query and identify all causal parameters and counterfactuals.
result Quantum oracles enable identification of all two-way joint counterfactuals and tighter bounds on higher-order counterfactuals.
New method tightens bounds on causation probabilities using independent datasets.
problem Challenging point identification of causation probabilities without strong assumptions.
method Imposes counterfactual consistency between SCMs constructed from independent datasets and uses conditional mutual information.
result Significantly tighter bounds on causation probabilities are established.
Proposes counterfactual explainability for causal attribution, extending variance analysis methods.
problem Lack of mechanistic understanding in existing tools for explaining complex models.
method Extends global sensitivity analysis methods to causal explanations using directed acyclic graphs.
result Developed methods to estimate counterfactual explainability and applied to income inequality analysis.
The paper proposes a method to precisely decompose confounders and estimate treatment effects.
problem Estimating treatment effects from observational data with confounder identification and balancing.
method Learning decomposed representations to identify and balance confounders and non-confounders.
result The method achieves more precise treatment effect estimation than existing methods.
Reduces selection bias in estimating individual treatment effects.
problem Selection bias in counterfactual reasoning.
method Auto-encoder with regularized loss based on Pearson Correlation Coefficient.
result Improves performance in estimating individual treatment effects.
New method explains anomalies in multivariate time series data.
problem Understanding and explaining anomalies in multivariate time series data.
method Counterfactual reasoning applied to MDI-detected anomalous intervals.
result Our method accurately identifies and explains anomalies in various extreme events.
New definition of patient-specific root causes of disease using counterfactuals.
problem Lack of rigorous mathematical formulation for automatic detection of root causes.
method Proposes a counterfactual definition matching clinical intuition and uses Shapley values for causal contribution scores.
result Adapts to disease prevalence, accounts for noisy labels, and admits fast computation.
The paper explores how missing data problems are related to causal inference.
problem Missing data in experiments makes causal inference difficult.
method The paper reinterprets missing data as a form of causal inference by considering counterfactual variables.
result Identification assumptions in missing data can be encoded using graphical models of counterfactual and observed variables.
DiffLOB models future market conditions for better decision-making.
problem Passive generative models cannot explore hypothetical market scenarios.
method Regime-conditioned diffusion model for counterfactual LOB generation.
result DiffLOB enables realistic and controllable generation of LOB trajectories.
This paper tackles confounding biases in data augmentation.
problem Mitigating spurious correlations and confounding variables in training data.
method Formal analysis and counterfactual data augmentation.
result Removing confounding biases leads to invariant features and better generalization.
Develops a new model for generating counterfactuals using incomplete data.
problem Lack of complete labels and data in medical image analysis.
method Semi-supervised deep causal generative model that infers missing values using causal inference.
result Generates realistic counterfactuals even with incomplete labels.
Proposes sparse local and regional counterfactual rules for robust recourses.
problem Challenges in counterfactual explanations, especially stability, synthesis, and implementation.
method Probabilistic framework using Random Forest to derive sparse local and regional counterfactual rules.
result Effective recourses derived from high-density regions, providing sparse and robust counterfactual rules.
Deep SCMs with deep learning infer counterfactuals from noisy data.
problem Inference of counterfactuals from noisy data in causal models.
method Normalizing flows and variational inference for deep SCMs.
result Tractable inference of exogenous noise variables for counterfactuals.
The paper introduces CPICFs for better counterfactual explanations in high-dimensional spaces.
problem Creating useful counterfactual explanations for complex machine learning models.
method Modeling individual knowledge and using conformal prediction intervals to identify informative counterfactuals.
result CPICFs provide more informative counterfactuals by considering individual knowledge and prediction uncertainty.
Predictive models can fail to generalize from training to deployment environments because of dataset shift, posing a threat to model reliability and the safety of downstream decisions made in practice. Instead of using samples from the target distribution to reactively correct dataset shift, we use graphical knowledge …
Paper introduces EnCounteR for estimating causal effects using encouragement data.
problem Challenges in estimating causal effects due to incomplete randomization and limited encouragement data.
method Introduces a generalized IV estimator, EnCounteR, leveraging both observational and encouragement data.
result Demonstrates superior performance of EnCounteR over existing methods.
Proposes a new model to identify unknown counterfactual outcomes for continuous variables.
problem Counterfactual inference for continuous outcomes with strong assumptions.
method Curvature Sensitivity Model to relax assumptions and provide informative bounds.
result Demonstrates effectiveness of the Curvature Sensitivity Model in identifying counterfactual outcomes.
New model predicts energy prices under different scenarios.
problem Complex causal relationships in energy markets with continuous regime changes.
method Augmented Time Series Structural Causal Models (ATSCM) integrating neural causal discovery.
result Enables novel counterfactual queries in energy markets.
Autoregressive flow models can perform causal discovery and inference tasks.
problem Causal inference tasks such as causal discovery and interventional predictions.
method Using autoregressive flow models to estimate causal directions and make predictions.
result Autoregressive flows can accurately perform causal inference tasks without restrictive assumptions.
When the performance of a machine learning model varies over groups defined by sensitive attributes (e.g., gender or ethnicity), the performance disparity can be expressed in terms of the probability distributions of the input and output variables over each group. In this paper, we exploit this fact to reduce the dispa…
In this paper we present a comprehensive view of prominent causal discovery algorithms, categorized into two main categories (1) assuming acyclic and no latent variables, and (2) allowing both cycles and latent variables, along with experimental results comparing them from three perspectives: (a) structural accuracy, (…
CDVAE estimates treatment effects over time by accounting for unobserved variables.
problem Estimating treatment effects over time in the presence of unobserved confounders.
method Causal Dynamic Variational Autoencoder (CDVAE) that addresses unconfoundedness and unobserved heterogeneity.
result CDVAE outperforms existing methods in estimating Conditional Average Treatment Effects (CATEs).
This study quantifies uncertainty in comparing treatments using RCTs with before-and-after measures.
problem Uncertainty in comparing treatments using RCTs with before-and-after measures.
method New statistical modeling principle called ETZ enables counterfactual uncertainty quantification (CUQ) in RCTs with Before-and-After Repeated Measures.
result CUQ typically has lower variability than factual uncertainty quantification and can be achieved in RCTs.
Causal autoregressive flows enable accurate causal inference and prediction.
problem Causal discovery and interventional predictions in machine learning.
method Autoregressive normalizing flows with fixed variable orderings.
result Causal models derived from autoregressive flows are identifiable and allow for accurate interventional and counterfactual predictions.
Proposes a new method for algorithmic recourse in confounded settings.
problem Provides actionable recommendations for individuals affected by automated decisions.
method Relaxes assumptions of no hidden confounding and additive noise, requiring only causal graph and confounding structure.
result Bounds the expected counterfactual effect of recourse actions, ensuring favourable outcomes in expectation.
AI uses language models to find instrumental variables quickly.
problem Finding valid instrumental variables is a challenging and heuristic process.
method Uses large language models to search for new instrumental variables through narratives and counterfactual reasoning.
result Demonstrates the effectiveness of multi-step and role-playing prompting strategies for LLMs.
Bounds on factual and counterfactual distributions under measurement error in discrete models.
problem Measurement errors in discrete data and their impact on inference.
method Expressing modeling assumptions as linear constraints and using linear programming to derive bounds.
result Sharp bounds on factual and counterfactual distributions for various models, including instrumental variable scenarios.
Paper harmonizes medical data using flow-based causal inference.
problem Heterogeneity in medical data from different sites and protocols.
method Flow-based normalizing method for counterfactual inference on structural causal models.
result Better cross-domain generalization compared to state-of-the-art algorithms.
Deep Causal Graphs model complex causal relationships using neural networks.
problem Limited applicability of parametric causal models to real-life datasets with non-linear relationships.
method Deep Causal Graphs, an abstract specification for neural networks to model causal distributions.
result Demonstrates expressive power in modelling complex interactions and provides true causal counterfactuals.
Method preserves quantiles to ensure fairness in data adaptation.
problem Ensuring fairness in classification and regression models.
method Quantile preservation in causal structural equation models.
result Fairness guarantees for classifiers trained on adapted data.
New framework explains ML credit scoring models using counterfactual examples.
problem Explaining complex ML models in finance for credit scoring.
method Adversarial counterfactual examples for tabular data.
result Proposes a method to generate realistic counterfactual examples for tabular data.
A new method improves data representation for diverse tasks.
problem Learning meaningful representations for tasks like batch correction and counterfactual inference.
method Contrastive Mixture of Posteriors (CoMP) method using misalignment penalties.
result CoMP achieves state-of-the-art performance on challenging tasks.
Develops c-GNF for personalized social science policy analysis.
problem Challenges in estimating causal effects and counterfactual inference in social sciences.
method causal-Graphical Normalizing Flow (c-GNF) method.
result c-GNF performs well in estimating causal effects and counterfactual inference.