causal representation learning

BISCUIT: Causal Representation Learning from Binary Interactions
Identifying the causal variables of an environment and how to intervene on them is of core value in applications such as robotics and …
Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M Asano, Taco Cohen, Efstratios Gavves
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Causal Representation Learning for Instantaneous and Temporal Effects in Interactive Systems
Causal representation learning is the task of identifying the underlying causal variables and their relations from high-dimensional …
Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M Asano, Taco Cohen, Efstratios Gavves
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CITRIS: Causal Identifiability from Temporal Intervened Sequences
In CITRIS we show under which conditions can one learn causal factors from sequences of high-dimensional data, e.g. images, in case one has access to the intervention targets.
Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M Asano, Taco Cohen, Stratis Gavves
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CITRIS: Causal Identifiability from Temporal Intervened Sequences
Intervention Design for Causal Representation Learning
In this paper, we take a first step towards bringing two fields of causality closer together, intervention design and causal …
Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M Asano, Taco Cohen, Efstratios Gavves
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Intervention Design for Causal Representation Learning