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Found 8 papers, 3 papers with code
Manifold Restricted Interventional Shapley Values
Shapley values are model-agnostic methods for explaining model predictions.
Unsupervised Model Selection for Time-series Anomaly Detection
The practical problem of selecting the most accurate model for a given dataset without labels has received little attention in the literature.
Testing Granger Non-Causality in Panels with Cross-Sectional Dependencies
This paper proposes a new approach for testing Granger non-causality on panel data.
Correcting Confounding via Random Selection of Background Variables
We propose a method to distinguish causal influence from hidden confounding in the following scenario: given a target variable Y, potential causal drivers X, and a large number of background features, we propose a novel criterion for identifying causal relationship based on the stability of regression coefficients of X on Y with respect to selecting different background features.
Causal Forecasting:Generalization Bounds for Autoregressive Models
Under causal sufficiency, the problem of causal generalization amounts to learning under covariate shifts, albeit with additional structure (restriction to interventional distributions under the VAR model).
Quantifying intrinsic causal contributions via structure preserving interventions
We propose a notion of causal influence that describes the `intrinsic' part of the contribution of a node on a target node in a DAG.
Causal structure based root cause analysis of outliers
We describe a formal approach to identify 'root causes' of outliers observed in $n$ variables $X_1,\dots, X_n$ in a scenario where the causal relation between the variables is a known directed acyclic graph (DAG).
Feature relevance quantification in explainable AI: A causal problem
We discuss promising recent contributions on quantifying feature relevance using Shapley values, where we observed some confusion on which probability distribution is the right one for dropped features.
