New model improves data augmentation for causal tasks.
problem Optimizing causal models robustly under Wasserstein distances.
method Proposes a new G-Causal Normalizing Flow architecture.
result Empirically outperforms standard generative models.
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48 results for “causal model”
We quantify causal bias in continuous treatment settings.
problem Identifying and quantifying causal bias in continuous treatment scenarios.
method Developed a novel characterization of causal bias in structural causal models, proving conditions for zero bias and efficient estimation.
result Causal bias can be estimated efficiently under certain structural equation restrictions, allowing for causal regularization of predictive models.
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.
New causal distances improve evaluation of causal discovery algorithms.
problem Evaluating causal discovery algorithms using graphical distances is limited.
method Defined causal distances based on causal distributions rather than graphical structure.
result Improved evaluation of causal discovery algorithms on synthetic and real-world datasets.
Bayesian model averaging improves causal effect estimation by averaging over multiple models.
problem Estimating causal effects under linear Structural Causal Models (SCMs).
method Bayesian model averaging using Gaussian scale mixture distributions for computational efficiency.
result Bayesian model averaging is optimal for causal effect estimation.
DoWhy-GCM extends causal inference in graphical models for diverse queries.
problem Addressing diverse causal queries in graphical causal models.
method Specify cause-effect relations via a causal graph, fit causal mechanisms, pose causal queries.
result Identification of root causes, attribution of causal influences, diagnosis of causal structures.
InGRA models for efficient Granger causality learning in multivariate time series.
problem Efficiently modeling Granger causality in large-scale multivariate time series data.
method Inductive GRanger causal modeling (InGRA) framework with prototypical Granger causal attention.
result InGRA detects common causal structures and infers Granger causal structures for new individuals.
ABCI infers causal models and queries simultaneously using Bayesian active learning.
problem Inference of causal models and effects in a two-stage process is inefficient and unnatural.
method Active Bayesian Causal Inference (ABCI) using Gaussian processes for sequentially designing experiments.
result ABCI is more data-efficient and accurate in learning causal queries from fewer samples.
Graph neural networks help infer causal effects from partially observable data.
problem Inferring causal effects from partially observable data.
method Theoretical analysis of GNN and SCM connections.
result Established a new model class for GNN-based causal inference.
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.
Flow models recover causal transformations from observational data and a valid ordering.
problem Causal inference with only observational data and a valid causal ordering.
method Flow models that can recover component-wise, invertible transformations of exogenous variables.
result Flow models outperform previous methods and deliver consistent performance across various structural causal models.
Identification of causal direction between a causal-effect pair from observed data has recently attracted much attention. Various methods based on functional causal models have been proposed to solve this problem, by assuming the causal process satisfies some (structural) constraints and showing that the reverse direct…
Proposes Causal Loss to improve machine learning models' causal inference.
problem Machine learning algorithms often fail to capture causal relationships when data is inconsistent.
method Introduces Causal Loss, a model-agnostic loss function that enhances interventional capabilities.
result Causal Loss improves non-causal associative models to have interventional capabilities.
Causal KL improves on existing metrics for evaluating causal models.
problem Insufficient discrimination between causal models using edit-distance and KL divergence.
method Introducing Causal KL, an augmented KL divergence that considers causal relationships.
result Causal KL variants effectively distinguish between observationally equivalent models.
CRN learns causal models using neural networks, scaling with variables and leveraging prior knowledge.
problem Challenges in learning causal models, especially scalability and leveraging prior knowledge.
method Causal Relational Networks (CRN) using continuous representations and previously learned information.
result CRN achieves high accuracy and quick adaptation to new causal models on synthetic data.
Generates realistic time-series data from causal models.
problem Simulate realistic time-series data from causal models.
method Adversarial Causal Tuning (ACT) methodology.
result ACT selects optimal causal models and quantifies goodness-of-fit.
Probabilistic models can handle causal inference without special tools.
problem Confusion over necessary tools for causal inference.
method Demonstrated through concrete examples that causal questions can be answered using standard probabilistic models.
result Causal questions can be addressed using standard probabilistic modelling and inference.
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 axiomatizes interventional probability distributions.
problem Causal inference and intervention.
method Axiomatization of interventional families.
result Markovian property of intervened distributions.
This work restricts hidden cardinality in causal models to infer causal relations.
problem Causal relations between variables with a common unobserved cause cannot be directly inferred.
method Derive inequality constraints from d-separation in causal models with known cardinalities of unobserved variables.
result Inference of causal relations is possible with additional assumptions about cardinalities.
Proposes TNCM-VAE for generating causal financial time series.
problem Lack of causal reasoning in market generators.
method Combines VAE with structural causal models, enforcing causal constraints through DAGs and using causal Wasserstein distance.
result Superior performance in counterfactual probability estimation, L1 distances as low as 0.03-0.10.
A framework for causal classification using uplift and causal heterogeneity methods.
problem Predicting the effect of interventions on different individuals from data.
method Causal classification framework using off-the-shelf supervised methods.
result Framework works for causal classification and uplift modelling, competitive with other methods.
A model learns causal representations from high-dimensional data.
problem Challenges in learning causal representations from high-dimensional data.
method Formulated a latent variable decoder model, Decoder BCD, for Bayesian causal discovery.
result Shows that using known intervention targets as labels helps in unsupervised Bayesian inference over structure and parameters.
New framework for dynamic causal graph modeling and effect estimation.
problem Dynamic changes in causal relationships over time.
method Score-based causal discovery with autoregressive model structure.
result Dynamic causal graph with time-varying causal relations.
Amortized Causal Discovery learns to infer causal graphs from time-series data, improving performance.
problem Inference of causal graphs from time-series data is inefficient due to fitting new models for each sample.
method Proposes Amortized Causal Discovery, a variational model that leverages shared dynamics across samples with different causal graphs.
result Significant improvements in causal discovery performance demonstrated experimentally.
In application domains such as healthcare, we want accurate predictive models that are also causally interpretable. In pursuit of such models, we propose a causal regularizer to steer predictive models towards causally-interpretable solutions and theoretically study its properties. In a large-scale analysis of Electron…
Relational Structural Causal Models enable causal reasoning about unseen object combinations.
problem Developing a model that can reason about causal and combinatorial aspects of unseen object combinations.
method Relational Structural Causal Models extend structural causal models to include relational variables and define identification criteria.
result Proposed relational neural causal models outperform non-relational baselines on simulated traffic scenes.
Despite the major advances taken in causal modeling, causality is still an unfamiliar topic for many statisticians. In this paper, it is demonstrated from the beginning to the end how causal effects can be estimated from observational data assuming that the causal structure is known. To make the problem more challengin…
Bivariate Causal Discovery for Categorical Data via Classification with Optimal Label Permutationstat.ML
Proposes a new classifier for causal discovery in categorical data.
problem Causal discovery for categorical data.
method Classification with optimal label permutation (COLP) and simple learning algorithm.
result Favorable performance compared to state-of-the-art methods.
New framework for cyclic quantum causal models with graph separation property.
problem Understanding causal relationships in feedback processes and exotic scenarios.
method Introducing a robust probability rule and a novel graph-separation property, p-separation.
result Established graph-separation properties for all consistent cyclic causal models.
Weak supervision enables learning causal representations from unstructured data.
problem Learning high-level causal representations from unstructured data like images.
method Weakly supervised setting with paired samples before and after interventions. Implicit latent causal models using variational autoencoders.
result Models can reliably identify causal structure and disentangle causal variables.
Optimizes causal effects on unknown graphs using Causal Entropy Optimization.
problem Optimizing causal effects in unknown causal graphs.
method Causal Entropy Optimization (CEO) framework that generalizes Causal Bayesian Optimization (CBO). Incorporates causal structure uncertainty in surrogate models and intervention selection.
result CEO achieves faster convergence to global optimum compared to CBO and improves upon sequential structure learning.
SCBMs model causal effects using low-dimensional bottlenecks.
problem Causal effect estimation in high-dimensional systems.
method Structural causal models with low-dimensional summary statistics.
result SCBMs provide a flexible framework for task-specific dimension reduction.
Study on forecasting methods and their causal implications.
problem Understanding the difference between statistical and causal risks in forecasting models.
method Introduce causal learning theory for forecasting, obtain uniform convergence bounds for VAR models.
result First theoretical guarantees for causal generalization in time-series forecasting.
New measures for causal entropy and information gain studied.
problem Quantifying causal relationships in machine learning.
method Formal study of causal entropy and information gain.
result Established fundamental properties and relationships.
New method discovers causal models from mixed time series data.
problem Discovering causal relationships from heterogeneous time series data.
method Variational inference-based framework MCD for linear and nonlinear causal models.
result Method outperforms state-of-the-art benchmarks in causal discovery tasks.
New method identifies causal relationships in presence of hidden variables.
problem Identifying causal relationships when hidden variables exist.
method Established sufficient conditions and introduced a search algorithm.
result Proved soundness and completeness of the search algorithm.
Proposes DCNAR for dynamic causal inference from neural time series.
problem Uncertainty and evolution of causal structure in real-world domains.
method Two-stage neural causal modeling integrating discovery and inference.
result Dynamic causal inferences are more stable and meaningful than alternatives.
Book introduces ML and AI for causal inference.
problem Uncertainty in causal relationships.
method Structural equation models, DAGs, SCMs, and Double/Debiased Machine Learning.
result Improved inference in causal models using predictive tools.
Bayesian causal inference method improves accuracy over traditional approaches.
problem Bayesian marginalisation over causal models is computationally infeasible.
method Decomposes structure marginalisation into causal orders and DAGs, using Gaussian processes for mechanisms and ARCO for orders.
result Method outperforms state-of-the-art in structure learning and inference.
SLdisco uses supervised learning to discover causal models from observational data.
problem Estimating causal effects from observational data with limited samples and sparse models.
method Supervised machine learning to map observational data to causal equivalence classes.
result SLdisco is more conservative, less sensitive to sample size, and provides better model inference.
Improved Granger causality method for dynamic time series data.
problem Traditional Granger causality method assumes constant causalities, failing to model dynamic causalities.
method Dynamic window-level Granger causality (DWGC) method with causality indexing.
result Improved DWGC method better detects window-level causalities.
Survey on combining causal models with deep generative models for improved explainability and fairness.
problem Deep generative models lack explainability, induce spurious correlations, and poor out-of-distribution extrapolation.
method Structural causal models (SCMs) combined with deep generative models to address shortcomings.
result Causal generative models offer robustness, fairness, and interpretability.
Designs a framework to transfer causal models between similar environments.
problem Transferability of causal models between different but similar environments.
method Object-oriented representations and continuous optimization for structure learning.
result Demonstrates advantages in gridworld settings using reinforcement learning.
CDPs visualize causal dependencies in AI models.
problem Understanding how AI models depend on data inputs causally.
method Developed Causal Dependence Plots (CDPs) to visualize causal dependencies.
result CDPs show causal changes in predictors and outcomes.
Hierarchical Causal Modelsstat.ME
Hierarchical causal models help understand cause and effect in nested data.
problem Learning cause and effect from nested hierarchical data.
method Extend structural causal models and causal graphical models with inner plates, develop graphical identification technique and estimation methods.
result Hierarchical data can enable causal identification even when non-hierarchical data cannot.
Differentiable causal discovery methods perform robustly under model violations.
problem Causal discovery algorithms struggle with real-world data due to unverifiable causal assumptions.
method Benchmarked differentiable causal discovery methods under eight model assumption violations.
result Differentiable causal discovery methods exhibit robust performance under Structural Hamming Distance and Structural Intervention Distance metrics.
Survey of deep causal models for industrial applications.
problem Estimating causal effects using deep learning.
method Deep causal models map covariates to a representation space and use objective functions for unbiased counterfactual data estimation.
result Comprehensive overview of deep causal models with industry applications.