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Found 4 papers, 2 papers with code
Learning Relational Causal Models with Cycles through Relational Acyclification
We introduce relational acyclification, an operation specifically designed for relational models that enables reasoning about the identifiability of cyclic relational causal models.
Non-Parametric Inference of Relational Dependence
Independence testing plays a central role in statistical and causal inference from observational data.
Relational Causal Models with Cycles:Representation and Reasoning
To facilitate cycles in relational representation and learning, we introduce relational $\sigma$-separation, a new criterion for understanding relational systems with feedback loops.
Correcting for Selection Bias in Learning-to-rank Systems
Click data collected by modern recommendation systems are an important source of observational data that can be utilized to train learning-to-rank (LTR) systems.
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