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A continuous Structural Intervention Distance to compare Causal Graphs
Understanding and adequately assessing the difference between a true and a learnt causal graphs is crucial for causal inference under interventions.
Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression
We propose to analyse the conditional distributional treatment effect (CoDiTE), which, in contrast to the more common conditional average treatment effect (CATE), is designed to encode a treatment's distributional aspects beyond the mean.
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