Domain Adaptation Causal Inference
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Domain adaptation by using causal inference to predict invariant conditional distributions sara magliacane thijs van ommen tom claassen stephan bongers philip versteeg joris m.
Domain adaptation causal inference. Domain adaptation by using causal inference to predict invariant conditional distributions. Domain adaptation by using causal inference to predict invariant conditional distributions reviewer 1 summary. In many cases these different distributions can be modeled as different contexts of a single underlying system.
This paper proposes a new approach to domain adaptation that relies on the identification of a separating feature set conditional on which the distribution of a variable of interest is invariant under a certain intervention. First we show how to formulate the problem of counterfactual infer ence as a domain adaptation problem and more specifically a covariate shift problem. A causal view zhang gong schölkopf 2015 invariant models for causal transfer learning rojas carulla et al 2016 domain adaptation as a problem of inference on graphical models zhang et al 2020.
Mooij an important goal common to domain adaptation and causal inference is to make accurate predictions when the distributions for the source or training domain s and target or test domain s differ. The contributions of our paper are as follows. Second we derive new fami lies of representation algorithms for counterfactual infer.
Why are we interested in the causal structure of a data generating process. Advances in neural information processing systems 31 nips 2018 supplemental authors. An important goal common to domain adaptation and causal inference is to make accurate predictions when the distributions for the source or training domain s and target or test domain s differ.
Causal inference and domain adaptation. Overview speakers related info overview. In many cases these different distributions can be modeled as different contexts of a single underlying system in which each distribution corresponds to a different perturbation of the system.
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