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Predictive models can fail to generalize from training to deployment environments because of dataset shift, posing a threat to model reliability and the safety of downstream decisions made in practice.
On the application of probability theory to agricultural experiments. essay on principles
Neyman, J. (1923) · 1923
Earlier work this paper cites.
Experimental and quasi-experimental designs for research
Campbell, D. T. and Stanley, J. C. (1963) · 1963
Earlier work this paper cites.
Estimating causal effects of treatments in randomized and nonrandomized studies
Rubin, D. B. (1974) · 1974
Earlier work this paper cites.
Multivariate generalizations of the wald-wolfowitz and smirnov two-sample tests
Friedman, J. H. and Rafsky, L. C. (1979) · 1979
Earlier work this paper cites.
Complete identification methods for the causal hierarchy
Shpitser, I. and Pearl, J. (2008) · 1979
Earlier work this paper cites.
Causal inference in the presence of latent variables and selection bias
Spirtes, P., Meek, C., and Richardson, T. (1995) · 1995
Earlier work this paper cites.
Measuring the complexity of classification problems
Ho, T. K. and Basu, M. (2000) · 2000
Earlier work this paper cites.
Improving predictive inference under covariate shift by weighting the log-likelihood function
Shimodaira, H. (2000) · 2000
Earlier work this paper cites.
Bayesian calibration of computer models
Kennedy, M. C. and O’Hagan, A. (2001) · 2001
Earlier work this paper cites.
Complexity measures of supervised classification problems
Ho, T. K. and Basu, M. (2002) · 2002
Earlier work this paper cites.
Hematologic changes in sepsis and their therapeutic implications
Goyette, R. E., Key, N. S., and Ely, E. W. (2004) · 2004
Earlier work this paper cites.
Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock
Kumar, A., Roberts, D., Wood, K. E., Light, B., Parrillo, J. E., Sharma, S., Suppes, R., Feinstein, D., Zanotti, S., Taiberg, L., et al. (2006) · 2006
Earlier work this paper cites.
Sparse gaussian processes using pseudo-inputs
Snelson, E. and Ghahramani, Z. (2006) · 2006
Earlier work this paper cites.
What counterfactuals can be tested
Shpitser, I. and Pearl, J. (2007) · 2007
Earlier work this paper cites.
Covariate shift by kernel mean matching
Gretton, A., Smola, A. J., Huang, J., Schmittfull, M., Borgwardt, K. M., and Schölkopf, B. (2009) · 2009
Earlier work this paper cites.
Probabilistic graphical models: principles and techniques
Koller, D. and Friedman, N. (2009) · 2009
Cited alongside, same era.
Causality
Pearl, J. (2009) · 2009
Cited alongside, same era.
Dataset shift in machine learning
Quionero-Candela, J., Sugiyama, M., Schwaighofer, A., and Lawrence, N. D. (2009) · 2009
Cited alongside, same era.
When training and test sets are different: characterizing learning transfer
Storkey, A. (2009) · 2009
Cited alongside, same era.
Infection in sickle cell disease: a review
Booth, C., Inusa, B., and Obaro, S. K. (2010) · 2010
Cited alongside, same era.
Transportability of causal and statistical relations: a formal approach
Pearl, J. and Bareinboim, E. (2011) · 2011
Cited alongside, same era.
Causes of effects and effects of causes
Pearl, J. (2015) · 2015
Later among the works it cites.
Counterfactual risk minimization: Learning from logged bandit feedback
Swaminathan, A. and Joachims, T. (2015) · 2015
Later among the works it cites.
Learning (predictive) risk scores in the presence of censoring due to interventions
Dyagilev, K. and Saria, S. (2015) · 2016
Later among the works it cites.
Domain adaptation with conditional transferable components
Gong, M., Zhang, K., Liu, T., Tao, D., Glymour, C., and Schölkopf, B. (2016) · 2016
Later among the works it cites.
Probabilistic programming in python using pymc3
Salvatier, J., Wiecki, T. V., and Fonnesbeck, C. (2016) · 2016
Later among the works it cites.
Bayesian nonparametric causal inference: Information rates and learning algorithms
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Bareinboim, E. and Pearl, J. (2012) · 2012
Cited alongside, same era.
Meta-transportability of causal effects: A formal approach
Bareinboim, E. and Pearl, J. (2013) · 2013
Cited alongside, same era.
An introduction to optimization
Chong, E. K. and Zak, S. H. (2013) · 2013
Cited alongside, same era.
Single world intervention graphs (swigs): A unification of the counterfactual and graphical approaches to causality
Richardson, T. S. and Robins, J. M. (2013) · 2013
Cited alongside, same era.
Domain adaptation under target and conditional shift
Zhang, K., Schölkopf, B., Muandet, K., and Wang, Z. (2013) · 2013
Cited alongside, same era.
Recovering causal effects from selection bias
Bareinboim, E. and Tian, J. (2015) · 2015
Cited alongside, same era.
Alaa, A. M. and van der Schaar, M. (2017) · 2017
Later among the works it cites.
Causal effect identification by adjustment under confounding and selection biases
Correa, J. D. and Bareinboim, E. (2017) · 2017
Later among the works it cites.
Reliable decision support using counterfactual models
Schulam, P. and Saria, S. (2017) · 2017
Later among the works it cites.
Scalable joint models for reliable uncertainty-aware event prediction
Soleimani, H., Hensman, J., and Saria, S. (2017) · 2017
Later among the works it cites.
Generalized adjustment under confounding and selection biases
Correa, J. D., Tian, J., and Bareinboim, E. (2018) · 2018
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A dual approach to scalable verification of deep networks
Dvijotham, K., Stanforth, R., Gowal, S., Mann, T., and Kohli, P. (2018) · 2018
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Certified defenses against adversarial examples
Raghunathan, A., Steinhardt, J., and Liang, P. (2018) · 2018
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Anchor regression: heterogeneous data meets causality
Rothenhäusler, D., Bühlmann, P., Meinshausen, N., and Peters, J. (2018) · 2018
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