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Found 11 papers, 2 papers with code
Embracing the black box: Heading towards foundation models for causal discovery from time series data
Our empirical findings suggest that causal discovery in a supervised manner is possible, assuming that the training and test time series samples share most of their dynamics.
Deep Learning-based Group Causal Inference in Multivariate Time-series
Causal inference in a nonlinear system of multivariate timeseries is instrumental in disentangling the intricate web of relationships among variables, enabling us to make more accurate predictions and gain deeper insights into real-world complex systems.
Simplified Concrete Dropout -- Improving the Generation of Attribution Masks for Fine-grained Classification
It utilizes the concrete dropout (CD) to sample a set of attribution masks and updates the sampling parameters based on the output of the classification model.
Time Series Causal Link Estimation under Hidden Confounding using Knockoff Interventions
We compare our results on synthetic data to those of a time series deconfounding method both with and without estimated confounders.
Causal Discovery using Model Invariance through Knockoff Interventions
Cause-effect analysis is crucial to understand the underlying mechanism of a system.
Causal Inference in Non-linear Time-series using Deep Networks and Knockoff Counterfactuals
Overall our method outperforms the widely used vector autoregressive Granger causality and PCMCI in detecting nonlinear causal dependency in multivariate time series.
Anomaly Attribution of Multivariate Time Series using Counterfactual Reasoning
There are numerous methods for detecting anomalies in time series, but that is only the first step to understanding them.
Counterfactual Generation with Knockoffs
Heuristic methods result in false-positive artifacts because the image after the perturbation is far from the original data distribution.
Analysing the Direction of Emotional Influence in Nonverbal Dyadic Communication: A Facial-Expression Study
Identifying the direction of emotional influence in a dyadic dialogue is of increasing interest in the psychological sciences with applications in psychotherapy, analysis of political interactions, or interpersonal conflict behavior.
Causal Inference in Nonverbal Dyadic Communication with Relevant Interval Selection and Granger Causality
Human nonverbal emotional communication in dyadic dialogs is a process of mutual influence and adaptation.
Image Denoising Via Collaborative Support-Agnostic Recovery
For sparse reconstruction, the likelihood of a tap being active in a patch is computed and refined through a collaboration process with other similar patches in the same group.
