Proposes a method to identify causal relationships using background knowledge.
problem Identifying causal relationships in the presence of background knowledge.
method Learning local structure using all types of causal background knowledge (direct, non-ancestral, ancestral). Criteria for identifying causal relationships based on local structure.
result Effective and efficient method for local structure learning and causal relationship identification.
Local method identifies causal relations in Markov equivalent DAGs.
problem Identifying causal relations when multiple DAGs are Markov equivalent.
method Graphical condition and local criteria for identifying causal paths.
result Local learning algorithm efficiently identifies causal variables.
CoDA augments data with counterfactuals from local causal structures.
problem Improving sample efficiency in RL with complex dynamic processes.
method Local causal models (LCMs) and Counterfactual Data Augmentation (CoDA).
result CoDA significantly improves RL agent performance in locally factored tasks.
New models infer causal effects from graph-based time-series data.
problem Inferring causal effects from graph-based relational time-series data.
method Proposes causal inference models leveraging graph topology and time-series data.
result Relational time-series causal inference models accurately estimate local causal effects of individual nodes.
LOAD discovers optimal adjustments locally for scalable causal inference.
problem Scalable causal inference for unknown causal graphs.
method Local Optimal Adjustments Discovery (LOAD) method.
result LOAD combines local and global approaches for efficient and accurate causal effect estimation.
New method removes interference bias in causal models.
problem Interference bias impedes causal effect identification in real-world settings.
method Novel definition of causal models with local interference, semi-parametric assumptions.
result True Average Causal Effect can be identified in certain semi-parametric models with local interference.
CaLoNet integrates spatial and local correlations for multivariate time series classification.
problem Ignoring spatial and local correlations in multivariate time series classification.
method Model spatial correlations using causality modeling, extract local correlations, integrate into graph neural network.
result Competitive performance compared to state-of-the-art methods on UEA datasets.
This paper is the continuation of [8]. We essentially prove that the familly of strongly causal spacetimes defined in [8] associated to generic achronal subsets in Ein contains all the examples of BTZ multi black-holes. It provides new elements for the global description of these multi black-holes. We also prove that a…
PLOT uses optimal transport to find neural site handles for causal abstraction.
problem Finding the relevant neural site for causal analysis is computationally challenging.
method PLOT employs optimal transport to localize causal variables from neural network outputs.
result PLOT efficiently finds intervention handles for causal abstraction in neural networks.
A new method selects covariates for causal effect estimation without strong assumptions.
problem Estimating causal effects without global causal structure learning and strong assumptions.
method Local covariate selection method that avoids pretreatment and causal sufficiency assumptions.
result The method achieves accurate causal effect estimation with improved computational efficiency.
b-LOAD extends local causal discovery with prior knowledge, improving causal effect estimation.
problem Local causal discovery struggles in data-scarce settings due to uncertainty and incomplete neighborhoods.
method b-LOAD incorporates prior knowledge directly into local structure learning, using Meek's rules to refine discovery.
result b-LOAD refines the admissible equivalence class and enlarges identifiable causal queries, improving causal effect estimation.
Calculates local Granger causality for Gaussian and nonlinear systems.
problem Understanding causal influence in complex systems.
method Vector autoregression and information-theoretic approach.
result Local Granger causality offers a robust and fast method for time-directed information transfer.
Polynomial delay algorithm tests causal models with hidden variables.
problem Testing causal models with hidden variables in polynomial delay.
method c-component local Markov property (C-LMP) and polynomial delay algorithm.
result First algorithm for poly-delay testing of CIs in causal graphs with hidden variables.
Dynamic Structural Causal Models handle time-dependent systems with cycles and latent confounding.
problem Representing and analyzing systems of Stochastic Differential Equations (SDEs) with DSCMs.
method Define time-splitting and subsampling operations to analyze DSCMs of SDEs, and apply existing causal discovery algorithms to time-series data.
result DSCMs provide a graphical Markov property for SDEs and enable identification of time-dependent causal effects.
This work investigates how GCNs should handle local structure discrepancies in testing nodes.
problem GCNs assume homophily but real graphs often have discrepancies in local structure.
method Using causal graph analysis, the study intervenes the graph structure to assess the local structure's impact on predictions.
result The method effectively enhances GCN predictions by eliminating local structure discrepancies.
LOCAL learns dynamic causal structures from time series data efficiently.
problem Challenges in discovering DAG from time series data due to dynamic nature and nonlinear interactions.
method LOCAL proposes a quasi-maximum likelihood-based score function and adaptive modules ACML and DGPL.
result LOCAL significantly outperforms existing methods in dynamic causal discovery.
M-CaStLe discovers causal structures in multivariate space-time data.
problem Challenges in causal graph discovery for high-dimensional gridded data.
method Generalizes CaStLe to multivariate analyses, using local embeddings and pooling spatial replicates.
result More accurately recovers multivariate causal structure and identifies physical dynamics.
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.
New algorithm identifies causal effects in latent confounding models.
problem Identifying causal effects in linear non-Gaussian models with latent confounding.
method Recursive algorithm using rank conditions on higher-order cumulants.
result Algorithm achieves comparable performance to overcomplete ICA without knowing the number of latent variables.
LCD improves causal discovery in high-dimensional gene data.
problem Predicting causal effects in large-scale gene expression data.
method Local Causal Discovery (LCD) with practical estimators, ICP algorithm inspiration, preselection method, and statistical tests.
result LCD estimator closely matches ICP's accuracy but is simpler and faster.
LDP speeds up causal discovery by partitioning, improving VAS recall and runtime.
problem Hard causal discovery in nonparametric settings with exponential complexity.
method Local Discovery by Partitioning (LDP) for causal inference around exposure-outcome pairs.
result LDP yields less biased and more precise estimates than baseline methods.
Develops a method for causal inference with noisy confounders.
problem Noisy measurements of confounders in treatment effects models.
method Local principal subspace approximation combining K-nearest neighbors matching and PCA.
result Estimators of treatment effects and counterfactual distributions are constructed.
Paper introduces v-CMC linking causality and utility.
problem Linking causality and utility for value theory.
method Developed a new causal independence principle (v-CMC) and proved its equivalence.
result Equivalence of local, global, and decomposition versions of v-CMC.
Gene regulatory networks play a crucial role in controlling an organism's biological processes, which is why there is significant interest in developing computational methods that are able to extract their structure from high-throughput genetic data. A typical approach consists of a series of conditional independence t…
BRACE generates efficient counterfactual explanations by integrating causal reasoning.
problem Challenges in traditional counterfactual explanations, especially neglecting causal relationships.
method Backtracking counterfactuals with causal reasoning.
result Our method provides deeper insights into model outputs and is computationally efficient.
Local learning method selects covariates for causal effect estimation in the presence of latent variables.
problem Estimating causal effects from nonexperimental data with latent variables.
method Local learning approach that identifies valid adjustment sets for causal relationships.
result Ensures soundness and completeness of causal effect estimation under standard assumptions.
Gene regulatory networks play a crucial role in controlling an organism's biological processes, which is why there is significant interest in developing computational methods that are able to extract their structure from high-throughput genetic data. Many of these computational methods are designed to infer individual …
FLOP algorithm speeds up causal structure learning for linear models.
problem Efficiently learning causal structures from discrete data.
method FLOP algorithm combines fast parent selection and iterative score updates.
result FLOP finds highly accurate causal structures with near-perfect recovery.
The Abstract Boundary singularity theorem was first proven by Ashley and Scott. It links the existence of incomplete causal geodesics in strongly causal, maximally extended spacetimes to the existence of Abstract Boundary essential singularities, i.e., non-removable singular boundary points. We give two generalizations…
A major problem for the learning of Bayesian networks (BNs) is the exponential number of parameters needed for conditional probability tables. Recent research reduces this complexity by modeling local structure in the probability tables. We examine the use of log-linear local models. While log-linear models in this con…
SCTL scales causal domain adaptation without prior knowledge.
problem Domain adaptation with covariate shift and invariances across domains.
method SCTL: scalable causal discovery algorithm based on Markov blanket.
result SCTL achieves scalable and robust domain adaptation.
Weather2vec learns representations to adjust for non-local confounding in air pollution studies.
problem Non-local confounding in evaluating environmental policies and climate events on health outcomes.
method weather2vec framework using balancing scores to learn representations of non-local information.
result The framework effectively adjusts for confounding in air pollution studies.
Efficient algorithm learns direct causes and effects from data.
problem Discovering direct causes and effects from data in a large space.
method ELCS algorithm using N-structures and Markov Blanket discovery.
result ELCS achieves better accuracy and efficiency than state-of-the-art methods.
New model learns causal world dynamics from state space models.
problem Lack of causal world models in neural world modeling.
method State Space Models (SSM) with attention mechanisms.
result SSM can learn causal models of environments with equivalent performance.
Bayesian method estimates causal effects with proxy networks.
problem Estimating causal effects with only proxy measurements of a latent interference network.
method Structural causal model with Block Gibbs sampler and Locally Informed Proposals.
result Accurately estimates causal effects even with noisy proxy networks.
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.
Local approach learns causal structure of linear Gaussian polytree models from interventional data.
problem Learning causal structure of linear Gaussian polytree models from interventional data.
method First learns the skeleton and then orients edges of the polytree using second order statistics and low-dimensional marginal distributions.
result Consistent and scalable approach that handles problems with thousands of nodes.
This article is concerned with causal structures, which are defined as a field of tangentially non-degenerate projective hypersurfaces in the projectivized tangent bundle of a manifold. The local equivalence problem of causal structures on manifolds of dimension at least four is solved using Cartan's method of equivale…
Local discovery method uncovers direct unfairness in complex systems.
problem Identifying causal pathways of unfairness in complex domains.
method Local discovery for direct discrimination (LD3) method.
result LD3 returns a valid adjustment set (VAS) for assessing unfairness.
A simple guide to understanding hierarchical causality in complex systems.
problem Understanding hierarchical causality in complex systems.
method Formalizing hierarchical causality in terms of actors and agents, with three key structures.
result The system requires three additional structures: causation classes, aggregation operators, and discrete event-time maps.
New scoring rule predicts causal relations from data with selection bias.
problem Discovering causal relations from independence constraints under selection bias and confounding.
method Local Y-Structure patterns and a scoring rule for Y-Structures.
result Y-Structure scoring rule successfully predicts causal relations in real-world data.
This paper evaluates deferring systems using causal inference.
problem Evaluating the impact of deferring systems on model accuracy is challenging.
method The paper uses a causal inference framework to evaluate deferring systems, distinguishing between scenarios with and without access to human predictions.
result The approach allows identifying causal effects of deferring strategies on predictive accuracy.
Improves causal graph learning on dependent binary data.
problem Challenges in learning causal graphical models from dependent binary data.
method Decorrelation-based approach using latent utility model and EM-like algorithm.
result Significant improvement in accuracy of causal graph learning.
We demonstrate the breakdown of several fundamentals of Lorentzian causality theory in low regularity. Most notably, chronological futures (defined naturally using locally Lipschitz curves) may be non-open, and may differ from the corresponding sets defined via piecewise C1-curves. By refining the notion of a causal…
Paper develops a new inequality for non-causal machine learning.
problem Current concentration inequalities cannot be applied to non-causal machine learning.
method Develops a framework for non-causal random fields and proves a Hoeffding-type inequality.
result Obtains a Hoeffding-type concentration inequality for non-causal random fields.
This paper develops a new method to model treatment effects that are heterogeneous across different quantiles.
problem Modeling treatment effects that vary across different quantiles of the outcome distribution.
method The paper combines quantile classification with local polynomial estimation to build a decision tree and forest.
result The proposed QLPRT and QLPRF methods provide a new way to estimate and infer heterogeneous treatment effects.
Paper tackles causal effect estimation in observational data with hidden variables.
problem Estimating causal effects in observational data with hidden confounders.
method Developed a theorem for local search to find superset of adjustment variables, proposing a data-driven algorithm.
result Proposed algorithm produces more accurate causal effect estimates than existing methods.
Bell's Theorem shows that quantum mechanical correlations can violate the constraints that the causal structure of certain experiments impose on any classical explanation. It is thus natural to ask to which degree the causal assumptions -- e.g. locality or measurement independence -- have to be relaxed in order to allo…