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Standard supervised learning breaks down under data distribution shift.
Statistical versus theoretical relations in economic macrodynamics.paper given at league of nations. reprinted in d.f. hendry and m.s. morgan (1995)
Frisch, R · 1938
Earlier work this paper cites.
The logic of causal inference: Econometrics and the conditional analysis of causation
Hoover, K. D · 1990
Earlier work this paper cites.
An algorithm for fast recovery of sparse causal graphs
Spirtes, P. and Glymour, C · 1991
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Causal discovery from changes
Tian, J. and Pearl, J · 2001
Earlier work this paper cites.
Optimal structure identification with greedy search
Chickering, D. M · 2002
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The im algorithm: a variational approach to information maximization
Barber, D. and Agakov, F. V · 2003
Earlier work this paper cites.
Measuring statistical dependence with hilbert-schmidt norms
Gretton, A., Bousquet, O., Smola, A., and Schölkopf, B · 2005
Earlier work this paper cites.
Analysis of representations for domain adaptation
Ben-David, S., Blitzer, J., Crammer, K., and Pereira, F · 2007
Earlier work this paper cites.
Causality
Pearl, J · 2009
Earlier work this paper cites.
Domain adaptation via transfer component analysis
Pan, S. J., Tsang, I. W., Kwok, J. T., and Yang, Q · 2010
Earlier work this paper cites.
On causal and anticausal learning
Schölkopf, B., Janzing, D., Peters, J., Sgouritsa, E., Zhang, K., and Mooij, J · 2012
Earlier work this paper cites.
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Arjovsky, M., Bottou, L., Gulrajani, I., and Lopez-Paz, D · 2019
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