New method combines gradient optimization with constraint-based techniques for causal discovery.
problem Causal discovery from observational data, especially with small sample sizes.
method Differentiable d-separation scores using percolation theory and soft logic for gradient-based optimization of conditional independence constraints. result Empirical evaluations show robust performance in low-sample regimes, surpassing traditional methods.
Proposes a new algorithm for learning continuous-time Bayesian network structures.
problem Lack of constraint-based algorithms for continuous-time Bayesian networks.
method Develops a constraint-based algorithm using statistical tests for conditional independence.
result The proposed algorithm is more accurate with variables having more than two values.
LP algorithm optimizes neural networks with architectural constraints.
problem Training neural networks with specific architectural constraints.
method Lagrangian optimization and saddle point search in adjoint space.
result LP algorithm is fully parallelizable and feasible for training deep networks.
The paper defines conditions for learning causal graphs from data with unobserved variables.
problem Learning causal graphs from data with unobserved variables.
method Formalizes constraint-based structure learning algorithms under conditions and assumptions.
result Natural family of algorithms output Markov equivalent graphs to the causal graph under faithfulness assumption.
New concept of illiquidity linked to credit risk, using Jarrow & Turnbull's analogy.
problem Understanding illiquidity in financial markets, especially with credit risk.
method Introduces a constraint-based notion of illiquidity, using Jarrow & Turnbull's foreign exchange analogy.
result A new mathematical framework for understanding illiquidity in financial markets.
Survey of integrating domain knowledge into DL models.
problem Improving DL model performance with limited data or complex functions.
method Five categories of approaches to inject domain knowledge into DL models.
result Survey identifies five main categories of approaches.
New algorithm reduces conditional independence tests needed for causal discovery.
problem Efficiently infer causal relations from observational data.
method Established an algorithm with complexity pO(s) tests. result Achieves exponent-optimality up to a logarithmic factor in terms of conditional independence tests.
A new method reduces CI tests for causal structure learning.
problem Exponential CI tests in constraint-based methods.
method Recursive Markov boundary-based approach.
result Significantly reduces CI tests compared to existing methods.
Proposes ConstraintMatch for semi-supervised clustering with unconstrained data.
problem Leveraging unconstrained data alongside constraints for clustering models.
method Semi-supervised context with pseudo-constraining and pseudo-labeling mechanisms.
result Demonstrates effectiveness of ConstraintMatch over baselines.
Three classes of algorithms to learn the structure of Bayesian networks from data are common in the literature: constraint-based algorithms, which use conditional independence tests to learn the dependence structure of the data; score-based algorithms, which use goodness-of-fit scores as objective functions to maximise…
New method trains neural nets without loss functions.
problem Training neural networks efficiently and without loss functions.
method Optimizer RRR derives steps from projections to local constraints, not gradients.
result Success in phase retrieval and neural networks, with novel partitioning of projections.
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.
Proposes methods to add constraints to neural networks to improve stability and generalization.
problem Improving stability and generalization of neural networks.
method Constraint-based regularization using stochastic gradient Langevin dynamics.
result Constraints help stabilize and improve the robustness of deep neural networks.
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.
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.
Bayesian method estimates dynamics from near-optimal trajectories.
problem Estimating dynamics from near-optimal expert trajectories in reinforcement learning.
method Constraint-based Bayesian approach integrating expert near-optimality.
result Significant improvements in decision-making and transfer success.
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. 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.
Constraint-based learning reduces the burden of collecting labels by having users specify general properties of structured outputs, such as constraints imposed by physical laws. We propose a novel framework for simultaneously learning these constraints and using them for supervision, bypassing the difficulty of using d…
Bayesian networks are probabilistic graphical models widely employed to understand dependencies in high dimensional data, and even to facilitate causal discovery. Learning the underlying network structure, which is encoded as a directed acyclic graph (DAG) is highly challenging mainly due to the vast number of possible…
New method for identifying causal relationships in financial time series data.
problem Identifying causal relationships in nonstationary financial time series data.
method Refined constraint-based causal discovery algorithm (CD-NOTS) for nonstationary time series data.
result CD-NOTS effectively identifies causal connections in financial applications.
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…
Support vector regression (SVR) is one of the most popular machine learning algorithms aiming to generate the optimal regression curve through maximizing the minimal margin of selected training samples, i.e., support vectors. Recent researchers reveal that maximizing the margin distribution of whole training dataset ra…
Constraint-based (CB) learning is a formalism for learning a causal network with a database D by performing a series of conditional-independence tests to infer structural information. This paper considers a new test of independence that combines ideas from Bayesian learning, Bayesian network inference, and classical hy…
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.
We address the problem of Bayesian structure learning for domains with hundreds of variables by employing non-parametric bootstrap, recursively. We propose a method that covers both model averaging and model selection in the same framework. The proposed method deals with the main weakness of constraint-based learning--…
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…
This paper deals with chain graphs under the alternative Andersson-Madigan-Perlman (AMP) interpretation. In particular, we present a constraint based algorithm for learning an AMP chain graph a given probability distribution is faithful to. We also show that the extension of Meek's conjecture to AMP chain graphs does n…
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 …
A new algorithm infers causal networks from data using topological thresholds.
problem Inferring causal networks from data.
method Two methods for determining topological thresholds: one to leave no disconnected nodes, the other to find a causal large connected component.
result The novel algorithm is faster and more accurate than the PC algorithm.
Canary optimizes VaR-constrained RL problems with a conservative bound using Cantelli's inequality.
problem Optimizing reinforcement learning policies under VaR constraints in dense cost regimes.
method Employing Cantelli's inequality to create a conservative and smooth bound on VaR constraints based on moments of cost returns. Extending trust-region framework for worst-case bounds on policy improvement and constraint violation.
result Canary reliably satisfies VaR constraints with fewest violations and earliest permanent satisfaction, while maintaining reward competitiveness.
New algorithm uncovers causal relations in non-stationary time series.
problem Discovering causal relations from non-stationary time series data.
method Constraint-based, non-parametric algorithm for semi-stationary time series.
result Algorithm PCMCIΩ identifies causal graph with CI tests. Two ML approaches learn local volatility surfaces from option prices, with GP being arbitrage-free.
problem Interpolating European vanilla option prices to create a local volatility surface.
method Gaussian process regression and neural net with arbitrage penalties.
result GP approach is arbitrage-free and yields best out-of-sample calibration error.
bnlearn is an R package which includes several algorithms for learning the structure of Bayesian networks with either discrete or continuous variables. Both constraint-based and score-based algorithms are implemented, and can use the functionality provided by the snow package to improve their performance via parallel c…
This paper deals with chain graphs under the Andersson-Madigan-Perlman (AMP) interpretation. In particular, we present a constraint based algorithm for learning an AMP chain graph a given probability distribution is faithful to. Moreover, we show that the extension of Meek's conjecture to AMP chain graphs does not hold…
New method identifies causal structure in exchangeable data.
problem Existing causal discovery methods struggle with i.i.d. data.
method Exchangeable data provides richer conditional independence structure.
result Exchangeable data allows for unique causal structure identification.
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.
Scalable method learns context-specific models for hundreds of variables.
problem Learning context-specific models for large numbers of variables.
method Order-based Markov chain Monte-Carlo search with context-specific sparsity assumption.
result Method scales to hundreds of variables and learns accurate models.
Fast nonparametric conditional independence testing via two-stage regression
problem Fast nonparametric conditional independence testing
method BLITZ (Broad-to-Local Independence Testing via residualiZation)
result Better null calibration than fast kernel, random-feature, and regression-based competitors
Maps between surfaces have degree constraints based on their Euler characteristics.
problem Constraints on the degree of maps between surfaces based on their Euler characteristics.
method Used the Kneser-Edmonds factorization theorem and provided a simple proof.
result Maps between surfaces have degree constraints based on their Euler characteristics.
We study the performance of Local Causal Discovery (LCD), a simple and efficient constraint-based method for causal discovery, in predicting causal effects in large-scale gene expression data. We construct practical estimators specific to the high-dimensional regime. Inspired by the ICP algorithm, we use an optional pr…
New method for estimating local structure around target nodes in DAGs.
problem Challenges in learning causal DAG structures in high-dimensional settings.
method Constraint-based method for estimating local structure around multiple target nodes.
result Consistency results for estimating local neighborhood structure of target nodes.
New classifiers ensure fairness by adjusting a base classifier's operating characteristics.
problem Ensuring fairness in binary classification with multiple group constraints.
method Intervening directly on a base classifier's operating characteristics using group-wise ROC convex hulls and post-processing.
result Methods satisfy multiple fairness constraints (DP, EO, PP) with minimal interventions and near-oracle accuracy.
It is commonplace to encounter heterogeneous or nonstationary data, of which the underlying generating process changes across domains or over time. Such a distribution shift feature presents both challenges and opportunities for causal discovery. In this paper, we develop a framework for causal discovery from such data…
Paper optimizes portfolio selection with ICX order constraints.
problem Minimizing portfolio variance with ICX order constraints.
method Optimal and efficient portfolios are derived in closed form.
result Closed-form solutions for optimal and efficient portfolios.
Semi-supervised clustering methods incorporate a limited amount of supervision into the clustering process. Typically, this supervision is provided by the user in the form of pairwise constraints. Existing methods use such constraints in one of the following ways: they adapt their clustering procedure, their similarity…
Algorithm improves recommendation subset selection in the presence of biases.
problem Maximizing submodular functions for recommendation in the presence of social biases.
method Algorithm for submodular maximization with fairness constraints.
result Algorithm provably outputs subsets with near-optimal utility and proportional representation.