IntDC framework uncovers causal relationships from non-interventional data.
problem Detecting causal relationships in non-interventional complex systems.
method Interventional Embedding Entropy (IEE) for causal strength measurement.
result IEE accurately finds causal edges and quantifies causal strength robustly.
This work tackles causal graph discovery with stochastic interventions to minimize the number of interventions.
problem Discovering the true causal graph from observational data with limited interventions.
method Proposes a stochastic intervention model and studies verification and search problems with approximation algorithms.
result Provides approximation algorithms with competitive ratios for verification and search problems.
FMI uses matching to mimic interventions for causal feature learning.
problem Challenges in causal discovery from observational data.
method Feature Matching Intervention (FMI) using matching to emulate perfect interventions.
result FMI outperforms in identifying causal features from observational data.
Estimates joint causal effects using single-variable interventions on nonlinear models.
problem Estimating joint causal effects from single-variable interventions.
method Identifiability result and practical estimator for decomposing causal effects.
result Joint effects can be inferred without joint interventional data for nonlinear additive models.
Model identifies causal structure from paired observational and interventional data with unknown soft interventions.
problem Identifying causal structure from observational and interventional data with unknown soft interventions.
method Proposes a scalable causal discovery model that aggregates subset-level PDAGs and applies contrastive cross-regime orientation rules.
result The model asymptotically recovers the identifiable PDAG and can orient additional edges compared to non-contrastive subset-restricted methods.
Paper axiomatizes interventional probability distributions.
problem Causal inference and intervention.
method Axiomatization of interventional families.
result Markovian property of intervened distributions.
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.
Study identifies causal relationships without direct supervision from unknown interventions.
problem Identify causal relationships from unknown interventions without direct supervision.
method General nonparametric setting with multiple datasets from unknown interventions.
result Identify ground truth latents and causal graph up to ambiguities.
Causal Bayesian networks interpret actions as interventions to connect models to real-world outcomes.
problem Connecting causal model predictions to real-world outcomes.
method Formal framework to interpret actions as interventions and prove impossibility results.
result No non-circular interpretation exists that satisfies natural desiderata without violating some.
The paper presents efficient methods for identifying causal graphs with latent variables.
problem Recovering causal graphs with latent variables while minimizing intervention costs.
method Two intervention cost models (linear and identity) are considered. Algorithms are provided for both models.
result Upper bounds on the number of interventions needed for recovery, and approximation factors for the linear cost model.
Extended LPCMCI learns causal models from interventional data to minimize prediction error.
problem Optimizing prediction of target variables using causal models.
method Combining observational and interventional causal discovery methods.
result Extended LPCMCI allows 60.9% optimal prediction of target variables compared to 53.6% with original LPCMCI.
Causal diagrams based on do intervention are useful tools to formalize, process and understand causal relationship among variables. However, the do intervention has controversial interpretation of causal questions for non-manipulable variables, and it also lacks the power to check the conditions related to counterfactu…
Method learns causal effects from multiple interventions in presence of unobserved confounders.
problem Disentangling causal effects from sets of interventions in the presence of unobserved confounders.
method Non-linear structural causal models with additive, multivariate Gaussian noise; algorithm that learns causal model parameters by pooling data from different regimes and maximizing combined likelihood.
result Identification proofs demonstrate that causal effects of single interventions can be learned from sets of interventions, even with unobserved confounders.
cCBO optimizes interventions in causal graphs under constraints.
problem Finding optimal interventions in causal graphs with constraints.
method Exploits graph structure, uses Gaussian processes, and sequentially selects interventions.
result Successful trade-off between fast convergence and feasibility of interventions.
Graph-coupled causal Bayesian optimization transfers information across related interventions.
problem Optimizing expensive systems where interventions are costly and causal effects are confounded.
method Ties intervention effects together through shared causal parameters, improving estimation.
result Information-gain and regret bounds show improved performance with shared mechanisms.
POSCMs extend SCMs for causal modeling with latent contexts.
problem Causal modeling with latent contexts and endogenous mechanisms.
method Kolmogorov-Arnold-Sprecher edge-functional decomposition for explicit parametrization.
result Identifiability of structure and mechanisms under latent context.
Algorithm detects causal change points quickly with adaptive interventions.
problem Detecting changes in causal models with interventions.
method Centralization technique, Kullback-Leibler divergence for intervention selection, adaptive intervention policy.
result Theoretical first-order optimality and validation through simulations and real-world studies.
Paper relaxes faithfulness assumption for causal discovery using interventions.
problem Violation of faithfulness assumption in natural systems leads to incorrect causal structure identification.
method Use intervention-immediacy faithfulness assumption to identify causal structures with hard interventions.
result Interventions contain information about causal structure that can identify causal structures when faithfulness is violated.
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.
DCBO optimizes interventions in evolving causal systems.
problem Optimal interventions in time-varying causal systems.
method Combines sequential decision making, causal inference, and GP emulation.
result DCBO identifies optimal interventions faster than competitors.
Bayesian method optimizes interventions for causal discovery.
problem Active interventions are needed for causal discovery when observational data is insufficient.
method Bayesian optimization-based approach using observational data and pre-experimental evaluation of interventions.
result Demonstrated effectiveness through various experiments.
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.
This paper identifies the minimal set of nodes for optimal conditional interventions in causal bandits.
problem Optimizing decision-making in causal bandits with conditional interventions.
method Graphical characterization and efficient algorithm to identify the minimal set of nodes.
result The proposed algorithm significantly prunes the search space and accelerates convergence rates.
New framework identifies causal models with arbitrary interventions, improving realism.
problem Identify causal models with realistic interventions.
method Theoretical framework for identifying causal models with arbitrary interventions.
result Identify causal models with arbitrary interventions, up to a higher-level abstraction.
Causal models bring many benefits to decision-making systems (or agents) by making them interpretable, sample-efficient, and robust to changes in the input distribution. However, spurious correlations can lead to wrong causal models and predictions. We consider the problem of inferring a causal model of a reinforcement…
This research tackles intervention-centric causal reasoning in learning agents by using meta-learning.
problem Learning agents lack the concept of interventions, making causal learning challenging.
method A meta-reinforcement learning algorithm is used to learn causal relationships from observational data.
result The approach enables agents to learn and manipulate the environment effectively.
Paper establishes identifiability and achievability for causal representation learning.
problem Identifying and recovering latent causal models and variables from observational and interventional data.
method Establishes identifiability and achievability using uncoupled interventions and a recovery algorithm.
result Guaranteed perfect recovery of latent causal model and variables under uncoupled interventions.
CAnDOIT discovers causal relationships using both observational and interventional time-series data.
problem Identifying causal relationships in the presence of hidden factors.
method CAnDOIT combines observational and interventional time-series data to reconstruct causal models.
result CAnDOIT effectively handles interventional data and enhances the accuracy of causal analysis.
Bayesian method estimates intervention effects in non-linear data.
problem Causal discovery from observational data with non-linear relationships.
method Gaussian Process Networks (GPN) with Bayesian estimation and Monte Carlo methods.
result Approach accurately identifies and reflects uncertainty of causal estimates.
New framework infers causal shifts in event sequences under out-of-domain interventions.
problem Inferring causal relationships in event sequences without considering out-of-domain interventions.
method Proposes a new causal framework to define ATE, designs an unbiased ATE estimator, and uses a Transformer-based neural network model.
result Demonstrates superior performance in ATE estimation and goodness-of-fit under out-of-domain-augmented point processes.
Review of methods enabling causal predictions under hypothetical interventions.
problem Need for predicting outcomes under hypothetical interventions in decision making.
method Systematic review of methods using causal inference for prediction models.
result Identified 13 methods for causal inference from observational data.
CAN learns conditional and interventional distributions from unlabeled data.
problem Learning conditional and interventional distributions from unlabeled data.
method CAN framework with LGN and CIGN architectures, equipped with an intervention mechanism.
result CAN generates both interventional and conditional samples without needing the causal graph.
This paper tackles causal interactions in mixtures of DAGs using interventions.
problem Learning causal interactions among variables governed by a mixture of causal systems.
method Establishes necessary and sufficient conditions for intervention size, designs an adaptive algorithm.
result Identifies true edges in a mixture of DAGs using optimal or near-optimal interventions.
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.
Method estimates causal effects from combined interventional and observational data.
problem Estimating causal effects from unobserved confounders.
method Causal reduction method replacing latent confounders with a single latent confounder.
result Improves estimation accuracy from combined data without observing all confounders.
New method identifies causal structures with fewer interventions.
problem Discovering causal structures from data.
method Active Intervention Targeting (AIT) method for learning causal DAGs.
result Significantly reduces the number of required interventions.
We develop a method to learn abstract causal graphs from interventional data.
problem Estimating causal models at fine granularity is impractical or undesirable.
method Novel graphical identifiability results and an efficient algorithm.
result Directly learns abstract causal graphs from interventional data.
GnIES recovers causal structure from unknown interventions.
problem Recovering causal structure from unknown interventions.
method Characterization of equivalence classes, greedy learning algorithm GnIES.
result GnIES recovers the equivalence class of the data-generating model.
New method disentangles mixed interventional and observational data in SEMs.
problem Learning causal relationships from mixed interventional and observational data.
method Developed a method to disentangle mixed interventional and observational data in linear SEMs with Gaussian noise.
result The method can identify causal graphs up to their interventional Markov Equivalence Class.
Expands experimental design for causal discovery from limited data.
problem Challenges in causal discovery from observational and interventional data.
method Bayesian optimal experimental design incorporating recent advances in causal discovery.
result Active causal discovery of large, nonlinear SCMs with both intervention target and value selection.
New method identifies causal variables from multi-node interventions, expanding on previous single-node approaches.
problem Inferring high-level causal variables from low-level observations under multiple interventions.
method Exploits variance trace of ground truth causal variables and regularizes for sparsity.
result First identifiability result for causal representation learning with multiple node interventions.
This paper tackles CRL for multi-node interventions, achieving identifiability guarantees.
problem CRL under unknown multi-node interventions, focusing on single-node assumptions.
method Establishes identifiability results for general latent causal models under stochastic interventions.
result Identifiability up to ancestors using soft interventions, perfect identifiability using hard interventions.
A new diffusion model encodes causal structures for better interventional sampling and edge inference.
problem Lack of causal analysis in standard diffusion models.
method Causality-encoded diffusion framework that trains conditional models consistent with a directed acyclic graph.
result The method enables accurate interventional sampling and edge inference, with theoretical guarantees and practical applications.
BaCaDI discovers causal structures from unknown interventions.
problem Inferring causal structures from unknown interventions with limited data.
method Bayesian framework with gradient-based variational inference.
result BaCaDI outperforms related methods in identifying causal structures and intervention targets.
MetaCaDI learns causal graphs and unknown interventions from few data instances.
problem Discovering causal mechanisms in systems with high data costs and unknown interventions.
method MetaCaDI is a Bayesian meta-learning framework that optimizes for rapid adaptation to new intervention targets.
result MetaCaDI significantly outperforms state-of-the-art methods in causal graph recovery and intervention target prediction.
Paper proposes scalable algorithm to estimate intervention targets in linear models.
problem Estimating intervention targets in linear models from observational and interventional data.
method The paper proposes a scalable algorithm that estimates intervention sites from the difference between precision matrices of observational and interventional datasets.
result The algorithm consistently identifies all intervention targets and updates observational Markov equivalence classes to interventional ones.
GACBO optimizes unknown causal graphs with interventions.
problem Optimizing a target variable on an unknown causal graph with interventions.
method Graph Agnostic Causal Bayesian Optimisation (GACBO) seeks to balance exploitation and exploration of causal structures and functions.
result GACBO outperforms baselines in simulated and real-world applications.
The paper tackles matching a desired mean in causal systems through shift interventions.
problem Matching a desired mean in causal systems.
method Defining Markov equivalence classes, proposing active learning strategies, deriving lower bounds.
result Proposed active learning strategies require fewer interventions than previous approaches, especially for certain graph classes.