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Evaluating novel contextual bandit policies using logged data is crucial in applications where exploration is costly, such as medicine.
A note on the use of proxy variables
Wickens, M. R · 1972
Earlier work this paper cites.
Proxy variables and specification bias
Frost, P. A · 1979
Earlier work this paper cites.
Estimation of regression coefficients when some regressors are not always observed
Robins, J. M., Rotnitzky, A., and Zhao, L. P · 1994
Earlier work this paper cites.
Robust estimation in sequentially ignorable missing data and causal inference models
Robins, J. M · 1999
Earlier work this paper cites.
Adjusting for nonignorable drop-out using semiparametric nonresponse models
Scharfstein, D. O., Rotnitzky, A., and Robins, J. M · 1999
Earlier work this paper cites.
Causality: models, reasoning and inference
Pearl, J · 2000
Earlier work this paper cites.
The covering number in learning theory
Zhou, D.-X · 2002
Earlier work this paper cites.
On the performance of kernel classes
Mendelson, S · 2003
Earlier work this paper cites.
Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study
Lunceford, J. K. and Davidian, M · 2004
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Edwards, J. K., Cole, S. R., and Westreich, D · 2015
Later among the works it cites.
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