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Found 5 papers, 1 papers with code
Invariance & Causal Representation Learning: Prospects and Limitations
In causal models, a given mechanism is assumed to be invariant to changes of other mechanisms.
Identifying Linearly-Mixed Causal Representations from Multi-Node Interventions
The task of inferring high-level causal variables from low-level observations, commonly referred to as causal representation learning, is fundamentally underconstrained.
Projecting infinite time series graphs to finite marginal graphs using number theory
In this work, we develop a method for projecting infinite time series graphs with repetitive edges to marginal graphical models on a finite time window.
Conditional Independence Testing with Heteroskedastic Data and Applications to Causal Discovery
We frame heteroskedasticity in a structural causal model framework and present an adaptation of the partial correlation CI test that works well in the presence of heteroskedastic noise, given that expert knowledge about the heteroskedastic relationships is available.
Discovering Causal Relations and Equations from Data
Discovering equations, laws and principles that are invariant, robust and causal explanations of the world has been fundamental in physical sciences throughout the centuries.
