arXiv research

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

New method quantifies intrinsic causal contributions in neural networks.

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…

Causal autoregressive flows enable accurate causal inference and prediction.

Generates counterfactuals in target domain from source domain observations.

New method identifies latent causal variables from observed data, overcoming indeterminacies.

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…

Federated framework learns causal states to predict counterfactuals without centralizing data.

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

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…

Intrinsically motivated reinforcement learning aims to address the exploration challenge for sparse-reward tasks. However, the study of exploration methods in transition-dependent multi-agent settings is largely absent from the literature. We aim to take a step towards solving this problem. We present two exploration m…

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

New framework TDRL identifies latent causal variables from sequential data.

New method detects anomalies in systems influenced by their environment.

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.

Reasoning based on causality, instead of association has been considered as a key ingredient towards real machine intelligence. However, it is a challenging task to infer causal relationship/structure among variables. In recent years, an Independent Mechanism (IM) principle was proposed, stating that the mechanism gene…

Visual objects are composed of a recursive hierarchy of perceptual wholes and parts, whose properties, such as shape, reflectance, and color, constitute a hierarchy of intrinsic causal factors of object appearance. However, object appearance is the compositional consequence of both an object's intrinsic and extrinsic c…

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.

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…

Granger causality is a fundamental technique for causal inference in time series data, commonly used in the social and biological sciences. Typical operationalizations of Granger causality make a strong assumption that every time point of the effect time series is influenced by a combination of other time series with a…

Compactness theorem for timed-metric spaces established.

Research aims to bridge statistical learning to causal models in AI.

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.

Study provides bounds for estimating intrinsic dimension using Gaussian kernels.

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…