CIB compresses variables causally, preserving key causal interactions.
problem Constructing causal variable abstractions in complex systems.
method Causal Information Bottleneck (CIB) method, extending IB to include causal structures.
result CIB produces causally interpretable abstractions that accurately capture causal relations.
New method identifies latent causal variables from observed data, overcoming indeterminacies.
problem Identifying latent causal variables from observed data, especially when latent variables are weight-variant.
method Introduces a novel identifiability condition for latent causal models, proposing SuaVE method.
result Identifies latent causal variables up to trivial permutation and scaling, demonstrating consistency and efficacy.
A neural network finds causal relationships among latent variables.
problem Learning causal structure among latent variables in high-dimensional data.
method Redundant Input Neural Network (RINN) with modified architecture and regularized objective function.
result The RINN method successfully recovers latent causal structure between input and output variables.
The paper investigates causal relationships in heart failure prediction using machine learning.
problem Understanding the causal relationships between clinical variables and heart failure.
method Proposes a new computational framework for causal structure discovery (CSD) of mixed-type clinical variables for binary disease outcomes.
result Feature importance from nonlinear classifiers strongly correlates with causal strength of variables, but not differentiating cause and effect.
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.
New model tackles causal bandits with dependent variables.
problem Understanding reward-maximizing interventions in causal networks with dependent variables.
method Introduces hierarchical causal bandit model with a contextual variable capturing interactions among variables.
result Derives nearly matching regret bounds for binary context in causal bandits with dependent arms.
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.
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.
The paper proposes a method to stabilize predictions by identifying causal variables using a seed variable.
problem Stable prediction across unknown test data with potential spurious correlations.
method Conditional independence test based algorithm using a seed variable to separate causal from non-causal variables.
result The algorithm precisely separates causal and non-causal variables for stable prediction across test data.
We consider the problem of learning causal models from observational data generated by linear non-Gaussian acyclic causal models with latent variables. Without considering the effect of latent variables, one usually infers wrong causal relationships among the observed variables. Under faithfulness assumption, we propos…
We use the score function for causal discovery, tackling challenges with hidden variables.
problem Causal discovery from observational data with hidden variables.
method Fine-tuning identifiability results, establishing conditions for inferring causal relations from the score, proposing a flexible algorithm.
result Empirical validation of the proposed algorithm for causal discovery on linear, nonlinear, and latent variable models.
Paper proposes a new method to identify causal graphs with latent variables using higher-order cumulants.
problem Estimating causal directed acyclic graphs with latent confounders.
method Uses higher-order cumulants to identify causal structures among observed and latent variables.
result Validates the proposed algorithm through simulations and real-world data.
New method infers causal effects without knowing control variables.
problem Inference errors when control variables are unknown.
method Proposes a method for inferring causal effects when control variables are unknown.
result Proves method yields asymptotically valid confidence intervals for average causal effects.
Researchers identify latent variables and causal structures from nonlinear hierarchical models.
problem Challenging task of identifying latent variables and causal structures from observational data, especially when relationships are nonlinear.
method Investigated nonlinear latent hierarchical causal models, developed identification criterion, and constructed an estimation procedure.
result Identifiability of causal structures and latent variables achieved under mild assumptions.
iCITRIS learns causal variables from interactive systems with instantaneous effects.
problem Identifying causal variables from temporal sequences with instantaneous effects.
method iCITRIS method for causal representation learning that handles instantaneous effects in intervened temporal sequences.
result iCITRIS accurately identifies causal variables and their causal graph from three interactive system datasets.
Causal discovery from data affected by latent confounders is an important and difficult challenge. Causal functional model-based approaches have not been used to present variables whose relationships are affected by latent confounders, while some constraint-based methods can present them. This paper proposes a causal f…
Proposes a new condition to estimate latent variable causal graphs from observed data.
problem Estimating causal structures when observed variables are not the underlying causal variables.
method Introduces Generalized Independent Noise (GIN) condition and a recursive learning algorithm.
result Shows that GIN helps locate latent variables and identify their causal structure.
The paper identifies causal effects in latent variable models using higher-order cumulants.
problem Challenges in identifying causal effects in latent variable models with latent confounders.
method Using higher-order cumulants, the paper addresses two challenging setups: a single proxy variable and underspecified instrumental variables.
result Causal effects are identifiable with a single proxy or instrument.
New findings show invariance alone isn't enough to identify latent causal variables.
problem Lack of theoretical insights for identifying latent causal variables when variables are latent.
method Assessed the connection between invariance and causal representation learning using impossibility results.
result Invariance alone is insufficient to identify latent causal variables.
Develops MgCSL for discovering causal structures in high-dimensional data.
problem Discovering causal relationships from high-dimensional data with complex interplay of variables.
method MgCSL uses sparse auto-encoders for coarse-graining and multi-layer perceptrons for detailed analysis, introducing simplified acyclicity constraints.
result MgCSL outperforms existing methods and finds explainable causal connections in fMRI datasets.
Paper shows identifiability of causal models with unobserved variables.
problem Identify latent variables in causal models with unobserved variables.
method Developed an autoencoding variational Bayes algorithm.
result Identifiability achieved with generalized faithfulness assumptions.
New RL environments help AI learn causal relationships from visual data.
problem Learning causal relationships from visual data for AI agents.
method Designing benchmark RL environments and evaluating representation learning algorithms.
result Explicitly incorporating structure and modularity improves causal induction in model-based RL.
The MAXENT principle helps merge datasets to infer causal effects.
problem Inferring causal effects from unobserved variables.
method Using the maximum entropy principle with causal sufficiency and faithfulness assumptions.
result Identifies causal edges among variables from merged datasets.
A new concept of causality for abstract phenomena.
problem Unclear definition of causality in real-life variables.
method Introduces 'phenomenological causality' based on elementary actions.
result Defines causal structure without hard-wired links.
FAIR-NN finds invariant variables for causal inference across diverse environments.
problem Nonparametric invariance and causal learning in regression models with varying joint distributions.
method FAIR-NN framework using adversarial optimization and neural networks.
result FAIR-NN identifies invariant variables and quasi-causal variables under minimal conditions.
Study develops method for estimating causal effects in continuous variables.
problem Lack of methods for estimating causal effects in continuous variables.
method Develops a method independent of data generating models for continuous variable interventions.
result Preserves identifiability of data and applies to any generating models.
Paper proposes methods to discover causal models with unobserved variables.
problem Discovering causal relationships in data with unobserved variables.
method Two methods leveraging prior knowledge for causal discovery in CAM-UV models.
result Accuracy of causal discovery improves with more prior knowledge.
The paper tackles causal disentanglement with linear models and interventions.
problem Identify latent variables in a causal model from observed data.
method Use linear transformations and interventions to uniquely identify latent variables.
result A single intervention on each latent variable is sufficient for identifying the latent causal model.
New algorithm groups variables by ancestral relationships to improve causal graph estimation accuracy.
problem Difficulty in estimating causal graphs with small sample sizes relative to variables.
method CAG algorithm groups variables based on ancestral relationships, reducing complexity and improving accuracy.
result CAG outperforms existing methods in estimation accuracy and computation time.
New causal measures improve feature selection in AI models.
problem Lack of causal interpretability in AI models.
method Introduces causal entropy and causal information gain to assess feature control.
result Demonstrates superiority of causal information gain in feature selection.
This work presents entropic constraints from DAGs with hidden variables.
problem Characterizing causal relations in systems with hidden variables.
method Entropic inequality constraints derived from e e e -separation relations. result These constraints can learn about true causal models from observed data.
This paper explores what causal structures can be distinguished by observational and interventional probing schemes.
problem Identifying causal structures with latent variables using observational and interventional data.
method Investigates the power of different probing schemes (observation vs. intervention) to distinguish causal structures.
result Two causal structures are indistinguishable if they share the same mDAG structure.
Extends linear structural causal models to include deterministic relations and latent confounders for causal discovery.
problem Causal discovery in linear SCMs with deterministic relations and latent confounders.
method Extended existing results to include deterministic relations and latent confounders, derived necessary and sufficient conditions for unique identifiability, proposed an algorithm for recovery.
result First work on identifiability results for causal discovery under latent confounding and deterministic relationships.
Paper proves causal direction can be inferred from data with limited randomness.
problem Inferring causal direction from observational data with limited randomness.
method Entropy measurement and structural causal models.
result Causal direction is identifiable for most causal models with limited entropy.
New method identifies latent variables with causal dependencies from observed data.
problem Identify latent variables with causal relationships from observed data.
method Linear causal disentanglement via higher-order cumulants, with perfect and soft interventions.
result Recovery of parameters via coupled tensor decomposition and polynomial equations.
DiCoLa recursively decomposes causal structure learning for latent variables.
problem Learning causal structures in high-dimensional settings with latent variables.
method Recursive decomposition framework for divide-and-conquer causal discovery.
result Theoretical soundness and completeness of DiCoLa framework.
Paper proposes learning causal graphs with only relevant variables.
problem Discovering causal relationships in large-scale graphs often includes irrelevant variables.
method Developed NSCSL algorithm to learn necessary and sufficient causal graphs (NSCG).
result NSCSL algorithm identifies relevant causal features for specific outcomes.
New score-based methods identify causal structures with latent variables.
problem Identifying causal structures involving latent variables.
method Score-based methods with identifiability guarantees.
result Score equivalence and consistency for latent variable causal models.
Causal inference concerns the identification of cause-effect relationships between variables, e.g. establishing whether a stimulus affects activity in a certain brain region. The observed variables themselves often do not constitute meaningful causal variables, however, and linear combinations need to be considered. In…
This paper tackles causal representation learning from multiple distributions without hard interventions.
problem Recovering latent causal variables and their relations from multiple distributions.
method Develops general solutions for causal representation learning without hard interventions, under sparsity constraints and suitable change conditions.
result Recovering the moralized graph of the underlying directed acyclic graph and latent variables related to the underlying causal model.
This thesis relaxes assumptions for causal discovery, making methods applicable to more complex systems.
problem Learning causal structures from observational data with latent variables.
method Alternative definition of k-Triangle Faithfulness for non-Gaussian distributions and uniform consistency proof.
result Uniform consistency of causal discovery algorithm under modified faithfulness assumption.
New method learns exogenous variable distributions for better causal optimization.
problem Maximizing target variables in structural causal models.
method Learn exogenous variable distributions to improve surrogate models' fidelity.
result Improves approximation of structural causal models and broader application scenarios.
Causal Component Analysis aims to recover latent variables with causal relationships.
problem Recover latent variables with causal relationships from observed mixtures.
method Introduces a likelihood-based approach using normalizing flows to estimate unmixing function and causal mechanisms.
result Demonstrates effectiveness through synthetic experiments in CauCA and ICA settings.
New method identifies causal variables from partially observed data.
problem Learning from unpaired observations with instance-dependent partial observability.
method Proposes two methods enforcing sparsity in the inferred representation.
result Establishes two identifiability results for linear and piecewise linear mixing functions.
Algorithm finds causal effects from observational data using auxiliary variables.
problem Estimating causal effects from observational data with confounders.
method Gradient-based optimization using auxiliary variables.
result Algorithm outperforms alternatives in estimating true causal effect.
Algorithm BGLM-OFU minimizes regret in combinatorial causal bandits with binary models.
problem Minimizing expected regret in combinatorial causal bandits with binary generalized linear models.
method BGLM-OFU algorithm based on maximum likelihood estimation for Markovian BGLMs, and causal inference techniques for linear models with hidden variables.
result Achieves O ( T log T ) O(\sqrt{T}\log T) O ( T log T ) regret for binary generalized linear models. Develops variable-lag Granger causality and Transfer Entropy for time series analysis.
problem Fixed time delay assumption in Granger causality and Transfer Entropy does not hold in many applications.
method Variable-lag Granger causality and Transfer Entropy, using optimal warping path of Dynamic Time Warping (DTW).
result Proposed methods perform better than existing methods in both simulated and real-world datasets.
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.