Timezone: »
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 M Mooij (University of Amsterdam)
More from the Same Authors
-
2022 Workshop: A causal view on dynamical systems »
Sören Becker · Alexis Bellot · Cecilia Casolo · Niki Kilbertus · Sara Magliacane · Yuyang (Bernie) Wang -
2021 Workshop: New Frontiers in Federated Learning: Privacy, Fairness, Robustness, Personalization and Data Ownership »
Nghia Hoang · Lam Nguyen · Pin-Yu Chen · Tsui-Wei Weng · Sara Magliacane · Bryan Kian Hsiang Low · Anoop Deoras -
2020 Workshop: Causal Discovery and Causality-Inspired Machine Learning »
Biwei Huang · Sara Magliacane · Kun Zhang · Danielle Belgrave · Elias Bareinboim · Daniel Malinsky · Thomas Richardson · Christopher Meek · Peter Spirtes · Bernhard Schölkopf -
2020 Poster: Active Structure Learning of Causal DAGs via Directed Clique Trees »
Chandler Squires · Sara Magliacane · Kristjan Greenewald · Dmitriy Katz · Murat Kocaoglu · Karthikeyan Shanmugam -
2020 Poster: Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models »
Tom Heskes · Evi Sijben · Ioan Gabriel Bucur · Tom Claassen -
2019 Poster: Sample Efficient Active Learning of Causal Trees »
Kristjan Greenewald · Dmitriy Katz · Karthikeyan Shanmugam · Sara Magliacane · Murat Kocaoglu · Enric Boix-Adsera · Guy Bresler -
2017 Poster: Causal Effect Inference with Deep Latent-Variable Models »
Christos Louizos · Uri Shalit · Joris M Mooij · David Sontag · Richard Zemel · Max Welling -
2016 : Joint Causal Inference on Observational and Experimental Datasets »
Sara Magliacane -
2016 Poster: Ancestral Causal Inference »
Sara Magliacane · Tom Claassen · Joris M Mooij -
2011 Poster: Learning sparse inverse covariance matrices in the presence of confounders »
Oliver Stegle · Christoph Lippert · Joris M Mooij · Neil D Lawrence · Karsten Borgwardt -
2011 Poster: Causal Discovery with Cyclic Additive Noise Models »
Joris M Mooij · Dominik Janzing · Tom Heskes · Bernhard Schölkopf -
2010 Poster: Probabilistic latent variable models for distinguishing between cause and effect »
Joris M Mooij · Oliver Stegle · Dominik Janzing · Kun Zhang · Bernhard Schölkopf -
2010 Poster: Causal discovery in multiple models from different experiments »
Tom Claassen · Tom Heskes -
2008 Poster: Nonlinear causal discovery with additive noise models »
Patrik O Hoyer · Dominik Janzing · Joris M Mooij · Jonas Peters · Bernhard Schölkopf -
2008 Poster: Bounds on marginal probability distributions »
Joris M Mooij · Hilbert J Kappen -
2008 Spotlight: Nonlinear causal discovery with additive noise models »
Patrik O Hoyer · Dominik Janzing · Joris M Mooij · Jonas Peters · Bernhard Schölkopf -
2008 Spotlight: Bounds on marginal probability distributions »
Joris M Mooij · Hilbert J Kappen