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Causal Information Splitting: Engineering Proxy Features for Robustness to Distribution Shifts
Statistical prediction models are often trained on data from different probability distributions than their eventual use cases.
Causal analysis of Covid-19 Spread in Germany
In this work, we study the causal relations among German regions in terms of the spread of Covid-19 since the beginning of the pandemic, taking into account the restriction policies that were applied by the different federal states.
Selecting causal brain features with a single conditional independence test per feature
We propose a constraint-based causal feature selection method for identifying causes of a given target variable, selecting from a set of candidate variables, while there can also be hidden variables acting as common causes with the target.
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