New framework uses background knowledge to speed up causal discovery.
problem Scalable causal discovery for large datasets.
method Utilizes background knowledge during causal discovery process.
result Background knowledge reduces computational requirements and improves structure quality.
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.
Paper presents a new dataset for testing causal discovery methods in industrial systems.
problem Lack of real-world datasets for evaluating causal discovery methods on time series data.
method Develops a dataset from an industrial system and its known causal graph.
result Provides a benchmark for evaluating causal discovery methods in complex systems.
New method recovers causal graphs from data scores in non-linear models.
problem Recovering causal graphs from data scores in non-linear models.
method Score matching algorithms and efficient Jacobian approximation.
result New method, SCORE, is competitive and faster than state-of-the-art methods.
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.
A new algorithm for robust causal discovery in small sample sizes.
problem Limited data leads to weak conditional independence tests in causal discovery.
method Proposes a k-PC algorithm that bounds conditioning set size for robust causal discovery. result The k-PC algorithm enables more robust causal discovery in small sample sizes. New assumptions help identify causal relationships in data.
problem Challenges in identifying causal relationships from observational data.
method Introduced typed directed acyclic graphs to constrain causal relationships.
result The proposed assumptions lead to significant gains in causal graph identification.
SNAP efficiently identifies causal effects without needing full graph learning.
problem Efficiently estimating causal effects on a subset of variables.
method Sequential Non-Ancestor Pruning (SNAP) framework.
result SNAP reduces independence tests and computation time without sacrificing causal effect estimations.
New method discovers causal relationships in large-scale data.
problem Discovering causal relationships in large datasets with thousands of variables.
method Factor Directed Acyclic Graphs (f-DAGs) combined with continuous optimization.
result Achieved causal discovery on thousands of variables.
This paper presents a new open source Python framework for causal discovery from observational data and domain background knowledge, aimed at causal graph and causal mechanism modeling. The 'cdt' package implements the end-to-end approach, recovering the direct dependencies (the skeleton of the causal graph) and the ca…
New measures assess differences in causal graphs' separations.
problem Evaluating causal discovery algorithms' output.
method Proposes new distance measures capturing causal graphs' separations.
result Proposed distances assess differences in causal graphs' separations.
CauScale efficiently discovers causal relationships in large graphs.
problem Efficiency bottlenecks in causal discovery for large graphs.
method Neural architecture with reduction unit and tied attention weights.
result Achieves 99.6% mAP on in-distribution data and 84.4% on out-of-distribution data.
Neural causal discovery methods fail to accurately uncover causal structures due to the faithfulness property.
problem Accuracy in neural causal discovery is limited, especially when distinguishing between existing and non-existing causal relationships.
method Systematic evaluation of neural causal discovery methods, focusing on their performance in finite sample regimes and their ability to recover ground-truth graphs.
result Neural networks lack the precision to reliably recover ground-truth causal graphs, even for small graphs and large sample sizes.
New method reduces errors in causal discovery from data.
problem Errors in causal discovery from limited data.
method Hierarchical wrapper for constraint-based algorithms.
result Significantly fewer tests, more accurate graphs, shorter run-times.
A model learns causal graphs from summary statistics of synthetic data.
problem Causal discovery algorithms are brittle with large sets of variables and limited data.
method A supervised model trained on synthetic data predicts causal graphs from summary statistics.
result The model generalizes well beyond its training set and runs on large graphs.
A new algorithm reduces CI tests for causal graph recovery.
problem Exponential CI tests limit causal discovery algorithms.
method CCPG (Causal Consistent Partition Graph) with polynomial CI tests.
result CCPG efficiently recovers causal graph with polynomial tests.
New distances for causal graphs improve evaluation of learned structures.
problem Difficulty in evaluating graphs learned by causal discovery algorithms.
method Developed a framework for causal distances, including new reachability algorithms.
result Improved distances are faster and more scalable than existing methods.
Efficient algorithms decide algebraic constraints of causal graphs.
problem Distinguish causal graphs with latent confounders.
method Study algebraic constraints and propose efficient algorithms.
result Decide equivalence or subset of algebraic constraints.
Algorithm recovers causal graphs in presence of latent confounders and selection bias.
problem Recovering causal graphs in the presence of latent confounders and selection bias.
method Iterative causal discovery (ICD) algorithm that relies on causal Markov and faithfulness assumptions.
result Sound and complete algorithm that recovers the equivalence class of the underlying causal graph.
This work integrates domain knowledge into A*-based causal discovery methods.
problem Efficiently incorporating domain knowledge into A*-based causal discovery methods.
method Integrates various types of domain knowledge into A*-based causal discovery methods, reducing the graph search space and improving computational gains.
result Small amounts of domain knowledge can dramatically speed up A*-based causal discovery and improve its performance and practicality.
New method identifies causal graphs with limited data and noise.
problem Identifying causal graphs from observational data is generally impossible.
method Using additional data from two environments with different noise statistics, and assuming Gaussian noise.
result The entire causal graph can be uniquely identified with a constant number of environments.
SPOT improves differentiable causal discovery by estimating skeleton posterior for latent confounders.
problem Scalable and accurate estimation of causal skeletons in the presence of latent confounders.
method SPOT (Skeleton Posterior-guided OpTimization) framework that estimates skeleton posterior and integrates it with differentiable causal discovery.
result SPOT enhances differentiable causal discovery by reducing the search space and improving accuracy.
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.
New method discovers mean and variance causal graphs from heteroscedastic data.
problem Understanding causal relationships in data with varying variance.
method Bayesian, moment-driven approach inferring separate mean and variance causal graphs.
result Accurately recovers mean and variance structures from heteroscedastic data.
DCCD-CONF discovers causal graphs with unmeasured confounders.
problem Discovering causal relationships in systems with unmeasured confounders.
method Differentiable learning of nonlinear cyclic causal graphs using interventional data.
result DCCD-CONF outperforms state-of-the-art methods in causal graph recovery and confounder identification.
We introduce a model for causal structure learning from multivariate functional data, even when graphs have cycles.
problem Discovering causal relationships from multivariate functional data with cycles.
method Functional linear structural equation model with a low-dimensional causal embedded space.
result The proposed model is causally identifiable under standard assumptions.
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.
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.
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.
The paper compares two methods for handling missing data in causal discovery.
problem Handling missing data in causal discovery algorithms.
method Test-wise deletion and multiple imputation.
result Multiple imputation is more challenging for causal discovery than for estimation.
Improved time series causal discovery with bootstrap aggregation and confidence measures.
problem Uncertainty estimation in time series causal discovery.
method Bootstrap aggregation and confidence measures for time series causal discovery.
result Bagged-PCMCI+ improves precision and recall compared to PCMCI+.
New algorithm identifies causal relationships from graphs, even with selection bias.
problem Identifying causal relationships from graphs with selection bias.
method Developed a measure-theoretic version of Pearl's causal calculus and a sound, complete identification algorithm.
result General measure-theoretic version of causal calculus allows for identification of causal relationships under selection bias.
New algorithm discovers causal graphs efficiently from observational data.
problem Discovering causal graphs from observational data efficiently.
method Approximating the score function using machine learning and applying scalable techniques.
result DAS algorithm reduces complexity and achieves competitive accuracy.
The paper develops a method to discover causal relations and predict material laws with uncertainty quantification.
problem Discovering causal relations and predicting material laws with uncertainty in civil engineering applications.
method The paper develops a causal discovery algorithm to infer causal relations among time-history data. It uses a deep neural network with dropout layers for uncertainty quantification and propagates predictions through a causal graph.
result The method accurately predicts material laws and quantifies uncertainty, as demonstrated in two numerical examples.
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.
The study learns causal graphs from time series data using entropy measures.
problem Learning causal graphs from time series data.
method Constraint-based framework, information-theoretic measures, generalized causation entropy, PC and FCI algorithms.
result The methods effectively construct causal graphs from time series data.
Extends causal discovery to group variables, improving performance in real-world applications.
problem Inferring cause-effect relationships from grouped data.
method Two-step approach: infer causal order and select models.
result Strong performance in simulations and real-world assembly line data.
We address the problem of causal discovery from data, making use of the recently proposed causal modeling framework of modular structural causal models (mSCM) to handle cycles, latent confounders and non-linearities. We introduce σ-connection graphs (σ-CG), a new class of mixed graphs (containing undirected, bidirected…
The paper shows that relaxing assumptions about causal graphs can lead to exponentially large equivalence classes.
problem The size of Markov equivalence classes under relaxed assumptions.
method Analytical proofs for three settings: sparse random directed acyclic graphs, uniformly random acyclic directed mixed graphs, and uniformly random directed cyclic graphs.
result Exponentially large lower bounds for the expected size of Markov equivalence classes.
Meta-learning improves Bayesian causal discovery by sampling from the posterior.
problem Difficulty in estimating the full posterior over causal structures due to large number of possible graphs and functional relationships.
method Proposes a Bayesian meta-learning model that encodes key properties of the posterior and allows for sampling causal structures.
result Meta-Bayesian causal discovery allows for reliable sampling from the posterior over causal structures.
DAG-FM discovers causal relationships from heterogeneous data.
problem Challenges in causal discovery from heterogeneous causal mechanisms.
method DAG-FM uses two specialized Transformer-based sub-modules and a robust tabular interaction block to model complex row-column interactions.
result DAG-FM achieves state-of-the-art performance on synthetic and real-world datasets.
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.
Cluster-DAGs improve causal discovery with prior knowledge.
problem Finding cause-effect relationships from high-dimensional data.
method Cluster-DAGs as prior knowledge framework, modified constraint-based algorithms Cluster-PC and Cluster-FCI.
result Cluster-PC and Cluster-FCI outperform baselines without prior knowledge.
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.
New method discovers causal relationships in sparse linear data.
problem Discovering cause-effect relationships in sparse linear data.
method Uses structural matrix to reconstruct data and identify causal structures without independence tests.
result Outperforms existing methods in sparse causal structure recovery.
RECLAIM discovers causal graphs in cyclic, noisy systems.
problem Discovering causal relationships in cyclic, noisy systems.
method RECLAIM uses EM with residual normalizing flows to handle cycles and noise.
result RECLAIM effectively discovers causal graphs in both synthetic and real-world datasets.
CausalRivers benchmarks causal discovery methods on real-world river discharge data.
problem Lack of in-the-wild evaluation of causal discovery methods on complex, real-world data.
method Introduces CausalRivers, a large-scale dataset of river discharge data for benchmarking.
result Demonstrates the utility of CausalRivers in evaluating causal discovery methods.
Proposes LLM-DCD for improved causal discovery from data.
problem Challenges in discovering causal relationships from observational data.
method Uses LLM to initialize DCD optimization, incorporating priors.
result Higher accuracy on benchmark datasets compared to state-of-the-art.