Inflation technique solves causal compatibility problem.
problem Determining if a graph is a plausible causal explanation for a distribution.
method Formal hierarchy of linear programming relaxations for causal compatibility.
result The inflation technique converges to a zero-error test for causal compatibility.
New method falsifies causal discovery results without ground truth.
problem Evaluation of causal discovery algorithms without ground truth data.
method Detects incompatibilities between causal graphs learned on different subsets of variables.
result Detection of incompatibilities can falsify wrongly inferred causal relations.
The problem of causal inference is to determine if a given probability distribution on observed variables is compatible with some causal structure. The difficult case is when the causal structure includes latent variables. We here introduce the inflation technique for tackling this problem. An inflation of a…
Study gluing of Lorentzian length spaces and their causal ladder properties.
problem Compatibility of Lorentzian amalgamation with length space properties.
method Conditions for gluing Lorentzian length spaces and criteria for causal ladder preservation.
result Gluing of Lorentzian length spaces yields again a Lorentzian length space under certain conditions.
New method bounds causal effects using local consistency of marginals.
problem Bounding causal effects due to unmeasured confounding.
method Enforces compatibility between marginals of causal models and data.
result Explicit algorithm and implementation of causal marginal polytope.
dcFCI discovers causal relationships robustly under latent confounding and mixed data.
problem Causal discovery under latent confounding and unfaithfulness.
method dcFCI integrates a new score to assess PAG compatibility, guided by FCI search.
result Significantly outperforms state-of-the-art methods in small and heterogeneous datasets.
Regularizes ML algorithms for robust multivariate analysis against distribution shifts.
problem Ensuring robustness of multivariate analysis algorithms against distribution shifts.
method Integrates a causal regularisation term into the loss function of multivariate analysis algorithms.
result Demonstrates improved out-of-distribution generalisation with reduced-rank regression and partial least squares.
New framework learns disentangled causal representations from observed labels.
problem Learning meaningful disentangled causal representations from observed data.
method ICM-VAE framework using flow-based diffeomorphic functions and causal disentanglement prior.
result Induces highly disentangled causal factors and improves robustness.
Categorical d-separation criterion simplifies probability graph analysis.
problem Detecting causal relationships in probability distributions.
method Introducing categorical definitions for causal models and d-separation.
result Abstract version of d-separation criterion applies to various probability theories.
ddml aids causal inference in econometrics with machine learning.
problem Estimation of causal effects with endogenous variables and unknown functional forms.
method Double/Debiased Machine Learning (DDML) in Stata.
result Monte Carlo evidence supports using DDML with stacking for causal inference.
grangersearch tests causal relationships in time series data.
problem Testing causal relationships between multiple time series.
method Exhaustive pairwise search, automatic lag order optimization, tidyverse integration.
result Automated Granger causality testing simplifies causal analysis.
New method recovers causal order from dependent data.
problem Causal discovery methods fail with shared volatility or common scale effects.
method Linear Mean-Independent Acyclic Model (LiMIAM) with mean-independence restrictions.
result Compatible causal order can be recovered from dependent disturbances.
Semidefinite tests detect latent causal structures efficiently.
problem Testing causal relations in the presence of latent variables.
method Semidefinite programming to test the signature of latent structures in observable covariance matrices.
result Semidefinite tests are computationally efficient and can detect latent causal structures.
Develops causal explanations for black-box classifiers.
problem Creating understandable explanations for complex models.
method Generative model with information-theoretic measures of causal influence.
result Learned latent factors influence classifier outputs.
NeuralCSA uses neural networks to analyze causal effects under unobserved confounding.
problem Challenges in causal inference from observational data due to unobserved confounding.
method Proposes a neural framework (NeuralCSA) for generalized causal sensitivity analysis.
result Demonstrates theoretical and empirical validity of NeuralCSA for causal inference.
Causal relationships in time series with latent variables are discovered using LPCMCI.
problem Discovering causal relationships in complex, time-series data with hidden variables.
method Evaluated LPCMCI algorithm for finding generators compatible with multi-dimensional, autocorrelated time series with latent variables.
result LPCMCI performs better than random guessing but is not optimal.
Method identifies causal relationships in data with latent variables.
problem Learning causal models from observational data with latent variables.
method Proposes a method to check causal paths and identify causal effects.
result Causal effects can be identified uniquely under certain conditions.
Detects causal scenarios with inequality constraints among classical correlations.
problem Classifying causal structures and identifying those with inequality constraints.
method Using d-separation, e-separation, incompatible supports, and HLP condition.
result Resolved all but three causal scenarios with up to 4 observed variables.
We define and study a new kind of relation between two diffeomorphic Lorentzian manifolds called {\em causal relation}, which is any diffeomorphism characterized by mapping every causal vector of the first manifold onto a causal vector of the second. We perform a thorough study of the mathematical properties of causal …
Study extends null distance concept to Lorentzian length spaces for spacetime analysis.
problem Understanding spacetime convergence and topology in Lorentzian geometry.
method Extend null distance concept to Lorentzian length spaces, study Gromov-Hausdorff convergence.
result First results on compatibility of null distance with synthetic curvature bounds in warped product Lorentzian length spaces.
OrphicX generates causal explanations for GNNs by isolating latent causal factors.
problem Generating interpretable causal explanations for complex graph neural networks.
method Develops a generative model and objective function to isolate latent causal factors, maximizing information flow.
result OrphicX effectively identifies causal semantics, significantly outperforming alternatives.
Proposes a method to estimate causal effects over a range of DAGs, addressing uncertainty in prior knowledge.
problem Uncertainty in prior knowledge of causal relationships between variables.
method Gradient-based optimization method providing bounds for causal queries over a collection of causal graphs.
result Bounds achieve good coverage and sharpness for causal queries in various settings.
Meta-learning model predicts intervention effects from uncertain causal graphs.
problem Estimating intervention effects when causal structures are uncertain.
method Model-Averaged Causal Estimation Transformer Neural Process (MACE-TNP) using meta-learning.
result MACE-TNP outperforms Bayesian baselines in predicting intervention distributions.
ZNet learns instrumental representations from covariates for causal inference.
problem Lack of valid instruments in observational studies.
method Representation learning approach that constructs instrumental representations from observed covariates.
result ZNet enables IV-based estimation without explicit instruments.
New method bounds causal effects in continuous distributions.
problem Estimating causal effects with general instrumental variables.
method Gradient-based optimization for computationally intractable bounds.
result Bounds capture causal effect when additive methods fail.
Bayesian method for causal discovery from unknown general interventions.
problem Learning causal DAGs from unknown interventions that modify parent sets.
method Bayesian approach with MCMC for approximating posterior DAGs and intervention targets.
result Bayesian method can identify DAGs and intervention targets up to equivalence classes.
Proposes PEID for analyzing synergistic causation in complex systems.
problem Challenges in identifying and analyzing synergistic causation in complex systems.
method Partial Effective Information Decomposition (PEID) framework.
result Unified and computable characterization of synergistic causal relations.
New pipeline for causal research in psychology and social sciences.
problem Underuse of causal approaches in psychology and social science.
method Formal specification of theories, reduction of complexity, estimation of causal effects.
result Facilitates scientific inquiry compatible with testing causal theories.
New methods infer causal structure from data without hidden variables.
problem Inferring causal structure from observational data with hidden variables.
method Introduces alternative independence tests and conditionally-additive-noise models.
result Can infer causal relations without assumptions about equation form or hidden variables.
New calculus on spacetimes for nonlinear differential equations.
problem Nonlinear differential equations on metric measure spacetimes.
method Introduces maximal weak subslope and variational calculus.
result Establishes a comparison theorem for nonlinear p-d'Alembertian. Comment refutes the deconfounder method's premise about ignorability.
problem The deconfounder method's premise about ignorability is incorrect.
method The deconfounder method proposes a variable making multiple causes conditionally independent controls for unmeasured multi-cause confounding.
result No fact about observed data alone can be informative about ignorability.
DECAF generates fair synthetic data by embedding causal relationships.
problem Generating fair synthetic data from biased training data.
method DECAF uses a GAN with a structural causal model to embed causal relationships and debias synthetic data.
result DECAF successfully removes bias and generates high-quality synthetic data.
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.
Develops a new method to measure causal effects in continuous and discrete settings.
problem Measuring direct causal effects in complex scenarios with continuous and interventionally changing variables.
method Probabilistic Easy Variational Causal Effect (PEACE) method, developed for both continuous and discrete cases.
result PEACE can measure causal effects under various conditions and is stable under small changes.
VISTA learns causal structures by integrating local subgraphs, improving accuracy and efficiency.
problem Efficiently learning causal structures from high-dimensional observational data.
method VISTA decomposes the global causal structure learning problem into local subgraphs based on Markov Blankets, integrating them via a weighted voting mechanism.
result VISTA achieves notable improvements in accuracy and efficiency over existing methods.
One of the goals of probabilistic inference is to decide whether an empirically observed distribution is compatible with a candidate Bayesian network. However, Bayesian networks with hidden variables give rise to highly non-trivial constraints on the observed distribution. Here, we propose an information-theoretic appr…
Valid causal inference with invalid instruments using majority or modal valid relationships.
problem Estimating causal effects in the presence of unobserved confounding and invalid instruments.
method Ensemble of instrumental variable estimators to estimate the modal prediction, achieving accurate estimates of conditional average treatment effects.
result Valid causal inference can be achieved with a majority or modal valid instrument-response relationship.
New methods estimate causal effects using front-door criterion in presence of unmeasured confounders.
problem Estimating causal effects in observational studies with unmeasured confounders.
method Developed novel one-step and targeted minimum loss-based estimators for front-door assumptions.
result Established conditions for root-n consistency and asymptotic linearity.
The paper explores how to make machine learning models robust to domain shifts.
problem Machine learning models are unreliable in domains different from training.
method Introducing a broad formal notion of invariance and causal structures.
result The true underlying causal structure of the data plays a critical role in robustness.
Unified tractability conditions for various compositional inference queries.
problem Analyzing tractability of probabilistic and causal inference queries.
method Algebraic perspective on circuits, focusing on semiring operators.
result Unified sufficient conditions for tractable composition of operators.
New method glues Lorentzian spaces, preserving curvature bounds.
problem Creating new spaces from existing ones in Lorentzian geometry.
method Introducing an amalgamation process for Lorentzian pre-length spaces.
result Gluing preserves upper curvature bounds in spacetimes.
We formalize causal separation in portfolio theory, deriving a closed-form projected Markowitz solution.
problem Portfolio optimization under causal separation conditions.
method Derive a closed-form solution for portfolio optimization using causal separation conditions.
result A closed-form projected Markowitz solution is derived under causal separation conditions.
The paper explores conjugate points in Lorentzian spaces, comparing different definitions and proving related theorems.
problem Understanding conjugate points in Lorentzian geometry.
method Introducing and comparing different definitions of conjugate points in synthetic Lorentzian length spaces.
result All defined notions of conjugate points are compatible with the smooth spacetime setting.
Proposes adversarial method to estimate Riesz representer.
problem Estimating causal parameters as linear functionals of an underlying regression.
method Adversarial framework using general function spaces.
result Nonasymptotic mean square rate proved for neural networks, random forests, and RKHS.
New method allows real-time audio synthesis using non-causal convolutions.
problem Real-time audio synthesis limitations due to offline model constraints.
method Post-training reconfiguration of non-causal models for real-time buffer-based processing.
result Non-causal streaming models can be transformed from offline-trained models without quality loss.
sparsebn learns large Bayesian networks from high-dimensional data.
problem Learning graphical models from large, high-dimensional datasets with interventions.
method Focuses on scalability and consistency in high-dimensional settings, learning causal networks from data.
result Achieves the goal of learning a causal network from data.
The paper classifies coverings and non-Hausdorff extensions of Misner spacetime.
problem Classifying coverings and non-Hausdorff extensions of Misner spacetime.
method Covering constructions of the punctured Minkowski plane, quotient spacetimes, and explicit embeddings.
result A natural family of spacetimes including the Hawking--Ellis extension and its universal-cover analogue.
A new ICA algorithm robustifies independent component analysis by accounting for group-wise stationary noise.
problem Tackles the challenge of independent component analysis in the presence of group-wise stationary confounding noise.
method Introduces coroICA, a novel ICA algorithm that extends the ordinary ICA model to incorporate group-wise confounding.
result Demonstrates improved performance and robustness of ICA in settings where other methods fail.