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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 Mooij

Thu Dec 06 07:45 AM -- 09:45 AM (PST) @ Room 210 #3

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. 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, or in causal terms, an intervention. We focus on a class of such causal domain adaptation problems, where data for one or more source domains are given, and the task is to predict the distribution of a certain target variable from measurements of other variables in one or more target domains. We propose an approach for solving these problems that exploits causal inference and does not rely on prior knowledge of the causal graph, the type of interventions or the intervention targets. We demonstrate our approach by evaluating a possible implementation on simulated and real world data.

Author Information

Sara Magliacane (IBM Research AI)
Thijs van Ommen (University of Amsterdam)
Tom Claassen (Radboud University Nijmegen)
Stephan Bongers (University of Amsterdam)
Philip Versteeg (University of Amsterdam)
Joris Mooij (University of Amsterdam)

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