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Found 7 papers, 7 papers with code
Evaluating Robustness to Dataset Shift via Parametric Robustness Sets
We give a method for proactively identifying small, plausible shifts in distribution which lead to large differences in model performance.
Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the Past
In this paper, we outline the difficulties that arise due to time structure and propose methodology for constructing identifying equations that can be used for consistent parametric estimation of causal effects in time series data.
Invariant Ancestry Search
Recently, methods have been proposed that exploit the invariance of prediction models with respect to changing environments to infer subsets of the causal parents of a response variable.
Local Independence Testing for Point Processes
Existing tests require strong model assumptions, e. g. that the true data generating model is a Hawkes process with no latent confounders.
Invariant Policy Learning: A Causal Perspective
We adopt the concept of invariance from the causality literature and introduce the notion of policy invariance.
Regularizing towards Causal Invariance: Linear Models with Proxies
In the case of two proxy variables, we propose a modified estimator that is prediction optimal under interventions up to a known strength.
Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values
In this article, we describe the algorithms for causal structure learning from time series data that won the Causality 4 Climate competition at the Conference on Neural Information Processing Systems 2019 (NeurIPS).
