TCDA separates observation space and causal assumptions for stable summaries.
problem Undefined or inadequate outcomes in modern data.
method Separates observation space, causal-model class, and topological representation.
result Identification and stability of causal effects through topology.
The paper analyzes the crash of stock and commodity markets during COVID-19 using Topological Data Analysis.
problem Identifying and understanding the dynamics and interdependence of stock and commodity markets during the COVID-19 crash.
method Topological Data Analysis (TDA) and Wasserstein Distance (WD) to identify crashes and compare market dynamics.
result Significant topological differences and interdependence between stock and commodity markets during the crash period.
Two algorithms track time-varying causality graphs online from VAR models.
problem Estimating time-varying causality graphs from multivariate time series data.
method Develops two online algorithms based on VAR models.
result Asymptotic performance similar to batch estimator, sublinear regret bounds.
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.
New algorithm disentangles latent features without strict assumptions.
problem Disentangling complex data-generating mechanisms into causally interpretable latent features.
method Linear CRL algorithm with topological ordering, pruning, and disentanglement.
result Recovering latent causal features up to an equivalence class under weaker assumptions.
New models infer causal effects from graph-based time-series data.
problem Inferring causal effects from graph-based relational time-series data.
method Proposes causal inference models leveraging graph topology and time-series data.
result Relational time-series causal inference models accurately estimate local causal effects of individual nodes.
Estimates multiple related causal graphs with shared causal order.
problem Discovering multiple related Gaussian DAGs with shared causal order.
method Proposes a l1/l2-regularized MLE for joint estimation of K linear structural equation models. result Joint estimator achieves better sample complexity and consistency in causal order recovery.
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.
Mastering the dynamics of social influence requires separating, in a database of information propagation traces, the genuine causal processes from temporal correlation, i.e., homophily and other spurious causes. However, most studies to characterize social influence, and, in general, most data-science analyses focus on…
New method estimates causal effects in complex spaces using topological structures.
problem Challenges in estimating causal effects in non-Euclidean spaces.
method Developed a topological causal inference framework using power-weighted silhouette functions of persistence diagrams.
result Successfully quantifies topological treatment effects across various complex outcomes.
Study examines two topologies on future causal completion of spacetimes.
problem Characterizing differences between two topologies on future causal completion.
method Systematic examination of the stronger topology τ+ on Geroch-Kronheimer-Penrose future completion IP(X) of spacetimes X. result Complete characterization of the difference in convergence between τ+ and the weaker topology. The causal structure of a strongly causal spacetime is particularly well endowed. Not only does it determine the conformal spacetime geometry when the spacetime dimension n >2, as shown by Malament and Hawking-King-McCarthy (MHKM), but also the manifold dimension. The MHKM result, however, applies more generally to spa…
TRA detects causal direction from bivariate data using geometric shapes.
problem Inferring causal direction from observational data is challenging and unreliable.
method TRA compares rank-based copula-standardized residual clouds to detect causal direction.
result TRA is robust and superior in detecting causal direction across various scenarios.
New method speeds up causal sensitivity analysis.
problem Bounding causal effects in unobserved confounding.
method Amortized approach using prior-data fitted networks.
result Orders of magnitude faster computation.
New topology defined from spacetime paths, reconstructing spacetime structure.
problem Reconstructing spacetime structure from path homotopy classes.
method Defining a topology on spacetime based on timelike and causal homotopy classes.
result The topology on spacetime is reconstructed from the space of homotopy classes.
Scoping review of EO-ML methods for causal inference in poverty geography.
problem Lack of thorough documentation and best practices for EO-ML methods in causal analysis.
method Comprehensive scoping review cataloging five principal approaches.
result Detailed protocol for integrating EO data into causal analysis.
NCFA uses deep learning and causal discovery to analyze complex data.
problem Analyzing complex, interdependent data with causal relationships.
method NCFA combines latent causal discovery and variational autoencoders.
result NCFA outperforms standard VAEs in sparsity, complexity, and causal interpretability.
This paper presents a sequential method to identify the topological ordering of causal DAGs using likelihood ratio scores.
problem Identifying the causal relationships in a data mining scenario with ambiguity of causal directions.
method A general sequential sorting procedure that orders variables one at a time, starting at root nodes, followed by children of the root nodes, and so on until completion. Simple likelihood ratio scores are used to decide the next node to append to the current partial ordering.
result The population version of the procedure provably identifies a true ordering of the underlying DAG under mild assumptions.
Study extends null distance concept to Lorentzian length spaces for spacetime analysis.
problem Understanding spacetime convergence and topology in Lorentzian geometry.
method Extend null distance concept to Lorentzian length spaces, study Gromov-Hausdorff convergence.
result First results on compatibility of null distance with synthetic curvature bounds in warped product Lorentzian length spaces.
This paper completes globally hyperbolic conformally flat spacetimes, proving they are topological manifolds.
problem Understanding the structure of spacetimes with specific properties.
method Analyzing globally hyperbolic conformally flat spacetimes, proving their causal completions are topological manifolds.
result Causal completions of globally hyperbolic conformally flat spacetimes are topological manifolds homeomorphic to S x [0, 1].
We provide a conceptual map to navigate causal analysis problems. Focusing on the case of discrete random variables, we consider the case of causal effect estimation from observational data. The presented approaches apply also to continuous variables, but the issue of estimation becomes more complex. We then introduce …
New method uses entropy to generate multiple plausible causal maps.
problem Learning causal relationships from noisy data can lead to artifacts in DAGs.
method Entropy-based inference to generate an ensemble of plausible causal graphs.
result Multiple causal maps consistent with underlying data variability.
Categorical d-separation criterion simplifies probability graph analysis.
problem Detecting causal relationships in probability distributions.
method Introducing categorical definitions for causal models and d-separation.
result Abstract version of d-separation criterion applies to various probability theories.
An important question that discrete approaches to quantum gravity must address is how continuum features of spacetime can be recovered from the discrete substructure. Here, we examine this question within the causal set approach to quantum gravity, where the substructure replacing the spacetime continuum is a locally f…
This paper proposes MM-DAGs for analyzing traffic congestion, learning multiple DAGs jointly.
problem Analyzing multi-modal traffic data with overlapping and distinct variables.
method Developed MM-DAGs for multi-task, multi-modal DAG learning, using multi-modal regression and CD measure.
result Proved the effectiveness of MM-DAGs in traffic congestion analysis.
Study uses ML and causal analysis to predict student performance factors.
problem Understanding socio-academic and economic factors affecting student performance.
method Employed machine learning techniques and causal analysis on 1,050 student profiles.
result Ridge Regression achieved robust predictions with MAE of 0.12 and MSE of 0.024.
Framework identifies causal factors of climate change using correlations and machine learning.
problem Understanding socioeconomic factors influencing carbon emissions and climate change.
method Three-step framework: correlation analysis, causal discovery, LLM interpretations.
result Adaptable solutions for data-driven policy-making and strategic decision-making.
It is commonly known that in Riemannian and sub-Riemannian Geometry, the metric tensor on a manifold defines a distance function. In Lorentzian Geometry, instead of a distance function it provides causal relations and the Lorentzian time-separation function. Both lead to the definition of the Alexandrov topology, which…
NeuralCSA uses neural networks to analyze causal effects under unobserved confounding.
problem Challenges in causal inference from observational data due to unobserved confounding.
method Proposes a neural framework (NeuralCSA) for generalized causal sensitivity analysis.
result Demonstrates theoretical and empirical validity of NeuralCSA for causal inference.
Causal analysis predicts market trends using time series data.
problem Predicting financial market trends using diverse time series data.
method Causal analysis based on lagged Pearson correlation applied to financial metrics.
result Discrimination of causal connections between different types of market data.
New topology preserves key properties of causal boundaries in spacetimes.
problem Defining and comparing causal boundaries in spacetimes with timelike boundaries.
method Introduced and utilized the Hausdorff closed limit topology (CLT) for spacetimes with timelike boundaries.
result Causal boundaries of spacetimes with conformal boundaries are homeomorphic to the causal boundary endowed with the CLT.
A reconstruction theorem in terms of the topology and geometrical structures on the spaces of light rays and skies of a given space-time is discussed. This result can be seen as part of Penrose and Low's programme intending to describe the causal structure of a space-time M in terms of the topological and geometrical…
New method identifies causal structure in count data using cumulants and path analysis.
problem Challenges in discovering causal structure from count data, especially due to non-identifiability.
method Poisson Branching Structural Causal Model (PB-SCM) with path analysis using high-order cumulants.
result Causal order is identifiable under specific conditions in PB-SCM using cumulant information.
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.
CDPs visualize causal dependencies in AI models.
problem Understanding how AI models depend on data inputs causally.
method Developed Causal Dependence Plots (CDPs) to visualize causal dependencies.
result CDPs show causal changes in predictors and outcomes.
Causal deep learning tackles causal inference using tensor factor analysis.
problem Addressing causal questions in data using neural networks.
method Tensor factor analysis and neural network architectures (causal capsules, tensor transformer, multilinear projection algorithm).
result Derives deep neural networks for causal inference with tensor factor analysis.
New method identifies cause-effect relations in multivariate time series data.
problem Identifying cause-effect relations in multivariate time series data.
method Fictitious vector autoregressive model to identify long-run relations and causality strength.
result High accuracy in identifying true cause-effect relations in simulations and climate change analysis.
Proposes a model for identifying edges in low-rank dynamical networks.
problem Inability of conventional methods to handle low-rank dynamical networks.
method Low rank dynamical network model with causal Wiener filtering.
result Consistent method for estimating all network edges.
Causality violations are typically seen as unrealistic and undesirable features of a physical model. The following points out three reasons why causality violations, which Bonnor and Steadman identified even in solutions to the Einstein equation referring to ordinary laboratory situations, are not necessarily undesirab…
This paper evaluates fractal dimension and persistent homology for neural network generalization.
problem Bounding and predicting the generalization gap of neural networks.
method Empirical evaluation of fractal dimension and persistent homology as generalization measures.
result Fractal dimension and persistent homology fail to predict generalization of models trained from poor initializations.
New method falsifies causal graphs using outlier events.
problem Inferring causal relationships from data is hard.
method Falsify candidate causal graphs based on outlier propagation.
result Statistical tests control false positives and have power guarantees.
Proposes TNCM-VAE for generating causal financial time series.
problem Lack of causal reasoning in market generators.
method Combines VAE with structural causal models, enforcing causal constraints through DAGs and using causal Wasserstein distance.
result Superior performance in counterfactual probability estimation, L1 distances as low as 0.03-0.10.
Framework isolates causal effects from time series data, improving accuracy under non-stationarity and autocorrelation.
problem Causal inference in non-stationary, autocorrelated time series data.
method Decomposes time series into trend, seasonal, and residual components; performs component-specific causal analysis.
result Framework more accurately recovers ground-truth causal structure than state-of-the-art baselines, especially under strong non-stationarity and temporal autocorrelation.
Bounds and sensitivity analysis for causal effects with MNAR confounders.
problem Estimating causal effects with missing outcome data.
method Assumption-free bounds and sensitivity analysis for outcome-independent MNAR.
result Valid bounds and sensitivity analysis methods for causal effect estimation.
It becomes increasingly popular to perform mediation analysis for complex data from sophisticated experimental studies. In this paper, we present Granger Mediation Analysis (GMA), a new framework for causal mediation analysis of multiple time series. This framework is motivated by a functional magnetic resonance imagin…
Develops variable-lag Granger causality for more accurate time series analysis.
problem Fixed time delay assumption in Granger causality does not fit many real-world applications.
method Variable-lag Granger causality, inferring with arbitrary time delays.
result Performs better than existing methods in coordinated collective behavior studies.
The paper shows how to learn causal representations with few environments and finite samples.
problem Learning causal representations from limited data and environments.
method Explicit, finite-sample guarantees with a logarithmic number of interventions.
result Consistent recovery of latent causal graph, mixing matrix, and unknown intervention targets.
Interpretable model for Granger causality using neural networks.
problem Inferring Granger causality in complex dynamical systems.
method Extension of self-explaining neural networks for multivariate Granger causality.
result Framework performs on par with baseline methods and better at inferring interaction signs.