DCM uses diffusion models to answer causal queries from observational data.
problem Answering causal queries from observational data alone.
method Diffusion models to learn causal mechanisms and generate latent encodings.
result Significant improvements over existing methods for causal query answering.
Paper reviews deep structural causal models for answering counterfactual queries.
problem Answering counterfactual queries using observational data with known causal structures.
method Deep generative models integrated with structural causal models.
result Provides insights into the capabilities and limitations of DSCMs.
Proposes a new query autocompletion method that maximizes retrieval performance.
problem Users often select suboptimal queries due to unknown best retrieval performance.
method Formulates query autocompletion as ranking item rankings, uses counterfactual learning.
result Empirical results show improved query suggestions for better retrieval performance.
VACA models graph data for causal inference without hidden confounders.
problem Causal inference in observational data with hidden confounders.
method Variational graph autoencoders for structural causal models.
result Accurately approximates interventional and counterfactual distributions.
Proposes a new method for estimating counterfactual treatment effects.
problem Uncertainty in identifying causal mechanisms from observational data.
method Introduces a parameterized family of causal mechanisms that generalize Gumbel-max, trained to minimize counterfactual effect variance.
result Trained mechanisms yield lower variance estimates of counterfactual treatment effects.
DoFlow models time series data for causal forecasting and anomaly detection.
problem Forecasting and causal reasoning in multivariate time series.
method Flow-based generative model over causal DAGs.
result Accurate interventional and counterfactual forecasting, anomaly detection.
ACE improves counterfactual explanations with fewer model queries.
problem Inefficient sampling for counterfactual explanations in machine learning models.
method Adaptive sampling combining Bayesian estimation and stochastic optimization.
result ACE achieves superior evaluation efficiency compared to state-of-the-art methods.
Algorithm bounds causal queries under selection bias.
problem Selection bias affects causal analysis.
method Proposes a new algorithm to address both identifiable and unidentifiable causal queries.
result The likelihood of available data is unimodal, enabling bounds on causal queries.
Cellina uses supervised disentanglement to predict cell behavior in tissues.
problem Querying counterfactuals on tissue graphs
method Cellina framework using supervised disentanglement
result Outperforms spatially-informed and non-spatial competitors
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.
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.
Develops methods for making deep learning models more interpretable by answering counterfactual questions.
problem Lack of interpretability in deep learning models, especially in high-stakes applications.
method Introduces causal interpretability, a framework for building models that are causally interpretable by design.
result Identifies a fundamental tradeoff between causal interpretability and predictive accuracy.
The study quantifies the information needed for causal queries at different levels of Pearl's hierarchy.
problem How much additional information is needed for interventional and counterfactual queries compared to observational queries?
method Formalized via query-class description length, using Kolmogorov complexity of answer oracles induced by SCMs.
result Binary acyclic SCMs show a quadratic gap between observational and interventional descriptions, and a logarithmic gap between interventional and counterfactual descriptions.
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 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.
Paper introduces Native Guide for generating time series counterfactual explanations.
problem Lack of explainability for time series data in AI systems.
method Model-agnostic, instance-based counterfactual generation for time series classification.
result Native Guide produces better counterfactual explanations than benchmarks.
In this work, we develop a technique to produce counterfactual visual explanations. Given a 'query' image I for which a vision system predicts class c, a counterfactual visual explanation identifies how I could change such that the system would output a different specified class c′. To do this, we select a 'dis…
We elaborate on using importance sampling for causal reasoning, in particular for counterfactual inference. We show how this can be implemented natively in probabilistic programming. By considering the structure of the counterfactual query, one can significantly optimise the inference process. We also consider design c…
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.
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.
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.
KaCGM models provide transparent causal inference from tabular data.
problem Limited auditability in deep causal models for tabular data.
method KaCGM uses Kolmogorov-Arnold Networks to parameterize structural equations, enabling direct inspection and visualization of causal mechanisms.
result KaCGM achieves competitive performance and interpretable causal effects in real-world applications.
Attack reveals model details from counterfactual explanations.
problem Extracting model details from counterfactual explanations.
method Adversary uses counterfactual explanations to build high-fidelity model.
result High-fidelity and high-accuracy model extraction possible.
Machine learning promises to revolutionize clinical decision making and diagnosis. In medical diagnosis a doctor aims to explain a patient's symptoms by determining the diseases \emph{causing} them. However, existing diagnostic algorithms are purely associative, identifying diseases that are strongly correlated with a …
TRAM-DAG models bridge interpretability and flexibility in causal modeling.
problem Modeling causal relationships in diverse data types while maintaining interpretability.
method Using transformation models (TRAMs) within structural causal models (SCMs) to handle various data types and maintain interpretability.
result TRAM-DAG models achieve equal or superior performance in causal queries across different levels of the causal hierarchy.
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.
We develop a learning principle and an efficient algorithm for batch learning from logged bandit feedback. This learning setting is ubiquitous in online systems (e.g., ad placement, web search, recommendation), where an algorithm makes a prediction (e.g., ad ranking) for a given input (e.g., query) and observes bandit …
CausalLongPFN predicts counterfactual outcomes from time-series data.
problem Predicting future outcomes under varying treatments in time-series data with confounding and heterogeneity.
method Prior-fitted network pretrained on synthetic episodes of temporal structural causal models.
result CausalLongPFN outperforms domain-trained models on factual and counterfactual prediction tasks.
COCOA improves credit assignment in reinforcement learning by measuring contributions to rewards.
problem Improving sample efficiency in reinforcement learning through better credit assignment methods.
method Counterfactual Contribution Analysis (COCOA) for precise credit assignment.
result COCOA achieves lower bias and variance compared to Hindsight Credit Assignment (HCA), improving reinforcement learning performance.
We develop SHOPPER, a sequential probabilistic model of shopping data. SHOPPER uses interpretable components to model the forces that drive how a customer chooses products; in particular, we designed SHOPPER to capture how items interact with other items. We develop an efficient posterior inference algorithm to estimat…
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.
Efficiently solves inverse classification problems for logistic and softmax models.
problem Finding instances that change classifier predictions.
method Closed-form solution for logistic regression, iterative optimization for softmax.
result Fast, exact solutions for high-dimensional instances and many classes.
Differentially private synthetic control estimates treatment effects while protecting privacy.
problem Estimating treatment effects on sensitive data without revealing individual information.
method Combines non-private synthetic control and differentially private empirical risk minimization.
result Private synthetic control produces accurate predictions with minimal privacy cost.
Optimizing an interactive system against a predefined online metric is particularly challenging, when the metric is computed from user feedback such as clicks and payments. The key challenge is the counterfactual nature: in the case of Web search, any change to a component of the search engine may result in a different…
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.
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.
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 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…