We consider learning ancestral causal relationships in high dimensions. Our approach is driven by a supervised learning perspective, with discrete indicators of causal relationships treated as labels to be learned from available data. We focus on the setting in which some causal (ancestral) relationships are known (via…
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48 results for “ancestral relationships”
CCHM algorithm learns BN structure with latent variables, improving causal effect measurement.
problem Latent variables cause spurious relationships in BN structure learning.
method Hybrid approach combining constraint-based and score-based learning, incorporating do-calculus.
result CCHM outperforms state-of-the-art in reconstructing true BN structure.
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 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.
Proposes a method to identify causal relationships using background knowledge.
problem Identifying causal relationships in the presence of background knowledge.
method Learning local structure using all types of causal background knowledge (direct, non-ancestral, ancestral). Criteria for identifying causal relationships based on local structure.
result Effective and efficient method for local structure learning and causal relationship identification.
Stable mixed graphsstat.OT
In this paper, we study classes of graphs with three types of edges that capture the modified independence structure of a directed acyclic graph (DAG) after marginalisation over unobserved variables and conditioning on selection variables using the -separation criterion. These include MC, summary, and ancestral grap…
ASCEND discovers causal relationships in multi-omics data by leveraging known hierarchical structure.
problem Causal inference in high-dimensional multi-omics data, especially when ignoring the hierarchical structure.
method Two-tiered divide-and-conquer strategy with ancestral conditioning sets.
result Achieves polynomial-time complexity and accurately recovers ancestral relationships.
New method for causal discovery using peeling algorithms for various data types.
problem Challenges in causal discovery due to unmeasured confounders.
method Two peeling algorithms (bottom-up and top-down) for causal discovery with generalized structural equation models.
result Valid discovery of causal relationships and parent-child effects in diverse data types.
New RL approach builds short ancestral recombination graphs.
problem Building short ancestral recombination graphs (ARGs).
method Reinforcement Learning applied to genetic sequences.
result RL can build ARGs as short as heuristic algorithms.
New method discovers causal relationships in confounded systems.
problem Discovering causal relationships in systems with unmeasured confounding variables.
method Differentiable algebraic constraints for continuous optimization of ADMGs.
result Effective method for causal discovery in confounded linear systems.
An efficient search-and-score algorithm for ancestral graphs using multivariate information scorescs.LG
Efficiently searches ancestral graphs using multivariate information.
problem Discovering causal relationships in graphs with latent variables.
method Greedy search-and-score algorithm with two-step approach.
result Outperforms existing methods on benchmark datasets.
Ancestral graph models, introduced by Richardson and Spirtes (2002), generalize both Markov random fields and Bayesian networks to a class of graphs with a global Markov property that is closed under conditioning and marginalization. By design, ancestral graphs encode precisely the conditional independence structures t…
New method for ancestral inference in branching processes with random environments.
problem Determining ancestor distribution parameters in branching processes with random environments.
method Generalized method of moments for ancestral inference.
result Limiting distribution of ancestor and offspring estimators decouple and converge to independent Gaussian variables under certain conditions.
In this paper, we show that Alexander polynomials for any 2-bridge knots are specializations of cluster variables. A key tool is an ancestral triangle which appeared in both quantum topology and hyperbolic geometry in different ways.
Study restricts causal graphs with expert knowledge.
problem Restricting causal graphs to include expert orientation knowledge.
method Prove properties, present new orientation rules, develop algorithms.
result Shows how to uniquely represent restricted essential ancestral graphs.
Paper discovers valid IVs from data without domain knowledge.
problem Inferring causal effects from observational data with latent confounders.
method Data-driven algorithm based on partial ancestral graphs (PAGs).
result Discovering valid IVs leads to accurate causal effect estimation.
One-hot encoding is a labelling system that embeds classes as standard basis vectors in a label space. Despite seeing near-universal use in supervised categorical classification tasks, the scheme is problematic in its geometric implication that, as all classes are equally distant, all classes are equally different. Thi…
Many biological characteristics of evolutionary interest are not scalar variables but continuous functions. Here we use phylogenetic Gaussian process regression to model the evolution of simulated function-valued traits. Given function-valued data only from the tips of an evolutionary tree and utilising independent pri…
Improved method for unbiased causal discovery in presence of unobserved confounding.
problem Unbiased data synthesis for causal discovery algorithms in the presence of unobserved confounding.
method Explicit block-hierarchical ancestral sampling to address limitations of implicit parameterization.
result Our approach fully covers the space of causal models, including those generated by implicit parameterization.
AGFN improves causal discovery by integrating expert feedback and handling latent confounding.
problem Inaccurate causal discovery due to unreliable expert knowledge and latent confounding.
method Ancestral GFlowNet (AGFN) is a reinforcement learning algorithm that iteratively refines a policy based on noisy expert feedback to infer ancestral graphs.
result AGFN converges to the true ancestral graph given accurate expert responses and outperforms baselines in structural Hamming distance and Bayesian Information Criterion.
Method infers parameters in complex diffusion processes.
problem Parameter inference in high-dimensional, non-linear diffusion processes.
method Differentiable score matching to approximate diffusion bridges, used in an importance sampler.
result Numerically stable framework for parameter inference and diffusion mean estimation.
Novel graphical models for time series with latent confounders improve causal inference.
problem Causal relationships and independencies in multivariate time series with unobserved confounders.
method Introduced a novel class of graphical models and characterized their properties.
result Novel graphs provide stronger causal inferences without additional assumptions.
A new algorithm learns MAGs from data more efficiently using entropy.
problem Learning MAGs from data is unstable and computationally expensive.
method Uses entropy estimation and refined Markov property to score MAGs.
result Algorithm is polynomial in number of nodes and outperforms existing methods.
Evolutionary Inference for Function-valued Traits: Gaussian Process Regression on Phylogeniesq-bio.QM
Biological data objects often have both of the following features: (i) they are functions rather than single numbers or vectors, and (ii) they are correlated due to phylogenetic relationships. In this paper we give a flexible statistical model for such data, by combining assumptions from phylogenetics with Gaussian pro…
The paper develops methods to bound causal effects using Partial Ancestral Graphs.
problem Bounding causal effects from observational data when true causal diagrams are unknown.
method Proposes a method using Partial Ancestral Graphs to derive bounds on causal effects from observational data.
result Demonstrates the effectiveness of the method with synthetic and real data examples.
We prove that the criterion for Markov equivalence provided by Zhao et al. (2005) may involve a set of features of a graph that is exponential in the number of vertices.
Efficient algorithms learn causal graphs with minimal interventions.
problem Learning causal relationships between observed variables in the presence of latents.
method Bi-criteria approximation goal combining intervention design and graph property testing.
result Achieve intervention cost within a small constant factor of the optimal.
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.
New causal models for growing networks avoid node deletion constraints.
problem Statistical models based on node exchangeability are not suitable for growing networks.
method Enumerated and partitioned causal directed acyclic graph (DAG) models over pairs of nodes.
result Simple model exhibits flexible power-law degree distributions and emergent phase transitions.
Machine learning models of music typically break up the task of composition into a chronological process, composing a piece of music in a single pass from beginning to end. On the contrary, human composers write music in a nonlinear fashion, scribbling motifs here and there, often revisiting choices previously made. In…
Causal graphs, such as directed acyclic graphs (DAGs) and partial ancestral graphs (PAGs), represent causal relationships among variables in a model. Methods exist for learning DAGs and PAGs from data and for converting DAGs to PAGs. However, these methods are significantly limited in that they only output a single cau…
Simplified identification methods for causal inference with arbitrary interventional distributions.
problem Estimating cause-effect relationships from data with experimental interventions.
method Using Single World Intervention Graphs and nested model factorization, we provide algorithms for identifying causal parameters from mixed observational and interventional distributions.
result Our algorithms are complete for certain types of interventional marginal distributions.
The paper develops CI tests for causal discovery in SDEs.
problem Inferring causal structure from stochastic dynamical systems.
method Developed CI constraints and a CI test for SDEs.
result Proposed CI test outperforms existing methods.
I-SPEC learns stable models from data without full causal knowledge.
problem Learning models that generalize well across shifts in environment.
method End-to-end framework using partial ancestral graph to learn stable interventional distribution.
result I-SPEC can learn robust models without full causal knowledge.
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.
We introduce a deep, generative autoencoder capable of learning hierarchies of distributed representations from data. Successive deep stochastic hidden layers are equipped with autoregressive connections, which enable the model to be sampled from quickly and exactly via ancestral sampling. We derive an efficient approx…
Recently, it has been shown that the Jones polynomial, in [LS19], and the Alexander polynomial, in [NT18], of rational knots can be obtained by specializing -polynomials of cluster variables. At the core of both results are continued fractions, which parameterize rational knots and are used to obtain cluster variabl…
Paper characterizes and represents pairwise causal background knowledge for improved causal inference.
problem Improving causal inference by handling pairwise causal constraints.
method Graphical characterization, direct causal clause (DCC), unified representation, MPDAG, polynomial-time algorithms.
result Pairwise causal background knowledge uniquely decomposes into MPDAG and DCCs, improving causal effect identification.
In this paper, we unify the Markov theory of a variety of different types of graphs used in graphical Markov models by introducing the class of loopless mixed graphs, and show that all independence models induced by -separation on such graphs are compositional graphoids. We focus in particular on the subclass of rib…
Generative AI can solve in-context learning problems using a martingale perspective.
problem Estimating when a conditional generative model can solve an in-context learning problem.
method Bayesian interpretation, ancestral sampling, generative predictive p-value.
result Developed a method to assess the suitability of CGMs for ICL problems using generative predictive p-values.
In this paper we discuss four problems regarding Markov equivalences for subclasses of loopless mixed graphs. We classify these four problems as finding conditions for internal Markov equivalence, which is Markov equivalence within a subclass, for external Markov equivalence, which is Markov equivalence between subclas…
This paper improves conditional sampling for VAEs by overcoming structural issues.
problem Computational intractability of conditional sampling in VAEs.
method Proposes two methods to address pitfalls in Metropolis-within-Gibbs (MWG) for VAEs.
result Improved performance on sampling tasks.
Constraint-based causal discovery from limited data is a notoriously difficult challenge due to the many borderline independence test decisions. Several approaches to improve the reliability of the predictions by exploiting redundancy in the independence information have been proposed recently. Though promising, existi…
A Characterization of Markov Equivalence Classes for Directed Acyclic Graphs with Latent Variablesstat.ME
Different directed acyclic graphs (DAGs) may be Markov equivalent in the sense that they entail the same conditional independence relations among the observed variables. Meek (1995) characterizes Markov equivalence classes for DAGs (with no latent variables) by presenting a set of orientation rules that can correctly i…
New algorithm learns causal graph to minimize regret in bandits without full structure.
problem Learning optimal decisions in bandits with unknown causal graph and latent confounders.
method Two-stage approach: first learns ancestors and necessary confounders, second applies standard bandit algorithm.
result No full causal structure needed for optimal decisions; only necessary confounders are crucial.
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.
Unified framework for inference in complex nonlinear processes.
problem Challenges in inferring nonlinear continuous stochastic processes with sparse observations and complex topologies.
method Neural Backward Filtering Forward Guiding (NBFFG) framework that constructs a variational posterior using a proxy linear-Gaussian process.
result Empirical results show NBFFG outperforms baselines on synthetic benchmarks and high-dimensional phylogenetic analysis tasks.
Recent work on mode connectivity in the loss landscape of deep neural networks has demonstrated that the locus of (sub-)optimal weight vectors lies on continuous paths. In this work, we train a neural network that serves as a hypernetwork, mapping a latent vector into high-performance (low-loss) weight vectors, general…