CICLAD efficiently mines frequent closed itemsets from data streams with minimal memory usage.
problem Mining frequent closed itemsets from data streams is resource-intensive.
method CICLAD is an intersection-based sliding-window FCI miner that optimizes memory usage while maintaining performance.
result CICLAD achieves significantly lower memory footprint compared to existing methods.
FCI method uses flow-based techniques to improve prediction confidence.
problem Limited applicability of exchangeable assumptions in predicting contaminated data.
method Adversarial flow to transform data into known distributions, then map to low-dimensional space.
result FCI produces effective predictive sets and accurate outlier detection.
This article contains detailed proofs and additional examples related to the UAI-2013 submission `Learning Sparse Causal Models is not NP-hard'. It describes the FCI+ algorithm: a method for sound and complete causal model discovery in the presence of latent confounders and/or selection bias, that has worst case polyno…
A hybrid method for causal discovery in latent confounders.
problem Estimating linear non-Gaussian models with latent confounders.
method Hybrid approach combining FCI and ICA.
result Hybrid method uniquely identifies causal relations under mild assumptions.
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.
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.
We consider the task of learning a causal graph in the presence of latent confounders given i.i.d.~samples from the model. While current algorithms for causal structure discovery in the presence of latent confounders are constraint-based, we here propose a score-based approach. We prove that under assumptions weaker th…
Discover causal structure from mixtures of DAGs using latent variable algorithms.
problem Discover causal structure from distributions arising from mixtures of DAGs.
method Causal structure discovery algorithms such as FCI for latent variables.
result Recover a 'union' of the component DAGs and identify varying conditional distributions.
A new personality-based recommender system tackles data sparsity without feedback.
problem Data sparsity without common feedback among users.
method Implicitly identifying users' personality type and incorporating it with personal interests and knowledge level.
result The model's effectiveness, especially in data sparsity situations, demonstrated on a real-world dataset.
We consider constraint-based methods for causal structure learning, such as the PC-, FCI-, RFCI- and CCD- algorithms (Spirtes et al. (2000, 1993), Richardson (1996), Colombo et al. (2012), Claassen et al. (2013)). The first step of all these algorithms consists of the PC-algorithm. This algorithm is known to be order-d…
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.
Many real datasets contain values missing not at random (MNAR). In this scenario, investigators often perform list-wise deletion, or delete samples with any missing values, before applying causal discovery algorithms. List-wise deletion is a sound and general strategy when paired with algorithms such as FCI and RFCI, b…
Causal processes in nature may contain cycles, and real datasets may violate causal sufficiency as well as contain selection bias. No constraint-based causal discovery algorithm can currently handle cycles, latent variables and selection bias (CLS) simultaneously. I therefore introduce an algorithm called Cyclic Causal…
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.
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 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.
We present two online causal structure learning algorithms which can track changes in a causal structure and process data in a dynamic real-time manner. Standard causal structure learning algorithms assume that causal structure does not change during the data collection process, but in real-world scenarios, it does oft…
Financial markets have been extensively studied as highly complex evolving systems. In this paper, we quantify financial price fluctuations through a coupled dynamical system composed of phase oscillators. We find a Financial Coherence and Incoherence (FCI) coexistence collective behavior emerges as the system evolves …
Discovering causal relationships from data is the ultimate goal of many research areas. Constraint based causal exploration algorithms, such as PC, FCI, RFCI, PC-simple, IDA and Joint-IDA have achieved significant progress and have many applications. A common problem with these methods is the high computational complex…
Tree-Query uses LLMs to discover causal relationships in a transparent, interpretable manner.
problem Error propagation in classical causal discovery methods and opaque, confidence-free behavior of recent LLM-based causal oracles.
method Tree-Query is a tree-structured, multi-expert LLM framework that reduces causal discovery to queries about backdoor paths and dependencies.
result Tree-Query provides interpretable judgments with robustness-aware confidence scores and improves structural metrics over LLM baselines.
We study the problem of discovering the simplest latent variable that can make two observed discrete variables conditionally independent. The minimum entropy required for such a latent is known as common entropy in information theory. We extend this notion to Renyi common entropy by minimizing the Renyi entropy of the …
New method improves causal discovery in time series with latent confounders.
problem Low recall in causal discovery for autocorrelated time series with latent confounders.
method Iterative procedure that includes causal parents in conditioning sets, using novel orientation rules.
result Significantly higher recall compared to existing methods, especially in strong autocorrelation cases.
Paper presents new algorithms for causal discovery with latent variables and overlapping datasets.
problem Causal discovery with latent variables and overlapping datasets.
method Introduces tiered FCI and tIOD algorithms for constraint-based causal discovery.
result The tIOD algorithm is more efficient and informative than the IOD algorithm.
We establish causal semantics for SDEs and develop methods to reason about them.
problem Understanding causal relationships in systems modeled by stochastic differential equations.
method We introduce a causal graph framework, Markov properties, and do-calculus for SDEs.
result We prove the σ-separation Markov property and do-calculus for causal SDEs. Algorithm recovers causal graphs from data with fewer tests.
problem Recovering causal graphs from observational data with latent confounders and selection bias.
method Iterative CI tests with increasing condition set sizes.
result Significantly fewer CI tests and condition sets compared to FCI.
Bayesian networks are typically faithful, with implications for causal inference.
problem Determining the typicality of faithfulness in Bayesian networks.
method Analysis of Bayesian networks over a given DAG, parametrized by conditional exponential families, and nonparametric conditional densities.
result The faithful Bayesian networks are dense and open with respect to the total variation metric, extending existing results for specific classes of Bayesian networks.