arXiv research

New method reveals true causal functions in nonlinear time series, not just scores.

Framework calculates positional influence in causal residual Transformers.

Quantum theory challenges traditional cause-effect relations, showing causal influences even without Bell inequality violations.

ACI identifies cause-effect relationships and causal influence ranges in dynamical systems.

Causal influence measures for machine learnt classifiers shed light on the reasons behind classification, and aid in identifying influential input features and revealing their biases. However, such analyses involve evaluating the classifier using datapoints that may be atypical of its training distribution. Standard me…

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…

CDA framework infers channel influence from aggregated data without user identifiers.

New framework IDOL identifies latent causal processes with instantaneous relations from time series data.

Fuzzy cognitive maps (FCMs) model feedback causal relations in interwoven webs of causality and policy variables. FCMs are fuzzy signed directed graphs that allow degrees of causal influence and event occurrence. Such causal models can simulate a wide range of policy scenarios and decision processes. Their directed loo…

Prediction-powered causal inference achieves smaller asymptotic variance than traditional methods.

New framework TDRL identifies latent causal variables from sequential data.

A new measure of causal influence quantifies intrinsic contributions in DAGs.

Novel method uses information theory to measure causal influences during transient neural events.

Study uses Granger causality to show investor sentiment influences stock prices.

Proposes a new estimator for causal mediation with continuous treatments.

Study reveals dynamic causal relationships between Ethereum transaction fees and economic subsystems.

ACI uses Bayesian data assimilation to trace causes from effects in complex systems.

Unified approach for estimating quantiles of potential outcomes using inverse estimating equations.

New framework detects directional influence in multivariate time series.

Develops causal explanations for black-box classifiers.

Develops variable-lag Granger causality for more accurate time series analysis.

Method identifies causal drivers from background features.

The problem of using observed correlations to infer causal relations is relevant to a wide variety of scientific disciplines. Yet given correlations between just two classical variables, it is impossible to determine whether they arose from a causal influence of one on the other or a common cause influencing both, unle…

A non-parametric method for ranking stock indices according to their mutual causal influences is presented. Under the assumption that indices reflect the underlying economy of a country, such a ranking indicates which countries exert the most economic influence in an examined subset of the global economy. The proposed …

A new algorithm infers causal networks from data using topological thresholds.

Identification of causal direction between a causal-effect pair from observed data has recently attracted much attention. Various methods based on functional causal models have been proposed to solve this problem, by assuming the causal process satisfies some (structural) constraints and showing that the reverse direct…

Estimates peer influence effects using embeddings for social networks.

The paper addresses bias amplification in prediction and decision-making using causal analysis.

Framework detects and mitigates data-poisoning attacks in causal effect estimation.

New method disentangles latent variables in nonstationary data.

This paper introduces a new method to better understand financial market causality.

Learning the influence structure of multiple time series data is of great interest to many disciplines. This paper studies the problem of recovering the causal structure in network of multivariate linear Hawkes processes. In such processes, the occurrence of an event in one process affects the probability of occurrence…

This work restricts hidden cardinality in causal models to infer causal relations.

New method quantifies intrinsic causal contributions in neural networks.

Simple linear models reveal complex cryptocurrency networks.

A widely applied approach to causal inference from a non-experimental time series $X$, often referred to as "(linear) Granger causal analysis", is to regress present on past and interpret the regression matrix $\hat{B}$ causally. However, if there is an unmeasured time series $Z$ that influences $X$, then this approach…

We propose a unified mechanism for achieving coordination and communication in Multi-Agent Reinforcement Learning (MARL), through rewarding agents for having causal influence over other agents' actions. Causal influence is assessed using counterfactual reasoning. At each timestep, an agent simulates alternate actions t…

Paper uses Gaussian processes to handle shared latent confounders in causal inference.

Combines structured inference and targeted learning to tackle causal inference challenges.

The paper addresses causal mediation analysis with post-treatment events, proposing robust estimators and efficient methods.

DoWhy-GCM extends causal inference in graphical models for diverse queries.

Develops variable-lag Granger causality and Transfer Entropy for time series analysis.

New method HNCI for evaluating treatment effects in network interference.

Framework identifies causal factors of climate change using correlations and machine learning.

We introduce a framework to infer lead-lag networks between the states of elements of complex systems, determined at different timescales. As such networks encode the causal structure of a system, infering lead-lag networks for many pairs of timescales provides a global picture of the mutual influence between timescale…

Proposes a flexible method for learning latent causal representations.

Corrects pseudo log-likelihood method issues in various applications.

A new method clusters heterogeneous subgroups for accurate causal learning.