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This work explores the idea of a causal contextual multi-armed bandit approach to automated marketing, where we estimate and optimize the causal (incremental) effects.
Estimating causal effects of treatments in randomized and nonrandomized studies
Rubin, D · 1974
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Incremental value modeling
Hansotia, B. and Rukstales, B · 2002
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The true lift model - a novel data mining approach to response modeling in database marketing
Victor, S. and Lo, Y · 2002
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Using control groups to target on predicted lift: Building and assessing uplift models
Radcliffe, N.J · 2007
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Direct importance estimation with model selection and its application to covariate shift adaptation
Sugiyama, M., Nakajima, S., Kashima, H., Bünau, P., and Kawanabe, M · 2007
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Stochastic linear optimization under bandit feedback
Dani, V., Hayes, T.P., and Kakade, S · 2008
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Large-scale behavioral targeting
Chen, Y., Pavlov, D., and Canny, J · 2009
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Web-scale bayesian click-through rate prediction for sponsored search advertising in microsoft’s bing search engine
Graepel, T., Candela, J.Q., Borchert, T., and Herbrich, R · 2010
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An empirical evaluation of thompson sampling
Chapelle, O. and Li, L · 2011
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Real-world uplift modelling with significance-based uplift trees
Radcliffe, N.J. and Surry, P · 2011
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Regret analysis of stochastic and nonstochastic multi-armed bandit problems
Bubeck, S. and Cesa-Bianchi, N · 2012
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Relational differential prediction
Nassif, H., Santos Costa, V., Burnside, E., and Page, D · 2012
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Thompson sampling for contextual bandits with linear payoffs
Agrawal, S. and Goyal, N · 2013
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Support Vector Machines for Differential Prediction
Kuusisto, F., Santos Costa, V., Nassif, H., Burnside, E., Page, D., and Shavlik, J · 2014
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Machine learning methods for estimating heterogeneous causal effects
Athey, S. and Imbens, G · 2015
Estimating the causal effects of marketing interventions using propensity score methodology
Rubin, D. and Waterman, R · 2015
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Estimating the causal impact of recommendation systems from observational data
Sharma, A., Hofman, J., and Watts, D · 2015
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A multiworld testing decision service
Agarwal, A., Bird, S., Cozowicz, M., Hoang, L., Langford, J., Lee, S., Li, J., Melamed, D., Oshri, G., Ribas, O., Sen, S., and Slivkins, A · 2016
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Malkov, Y. and Yashunin, D · 2016
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Diversifying music recommendations
Nassif, H., Cansizlar, K.O., Goodman, M., and Vishwanathan, S.V.N · 2016
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Bandits with unobserved confounders: A causal approach
Bareinboim, E., Forney, A., and Pearl, J · 2015
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Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
Imbens, G. and Rubin, D · 2015
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Counterfactual estimation and optimization of click metrics in search engines: A case study
Li, L., Chen, S., Kleban, J., and Gupta, A · 2015
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Score as you lift (SAYL): A statistical relational learning approach to uplift modeling
Nassif, H., Kuusisto, F., Burnside, E., Page, D., Shavlik, J., and Santos Costa, V
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Uplift modeling with ROC: An SRL case study
Nassif, H., Kuusisto, F., Burnside, E.S., and Shavlik, .J
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Batch learning from logged bandit feedback through counterfactual risk minimization
Swaminathan, A. and Joachims, T
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Adaptive, personalized diversity for visual discovery
Teo, C.H., Nassif, H., Hill, D., Srinavasan, S., Goodman, M., Mohan, V., and Vishwanathan, S.V.N · 2016
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An efficient bandit algorithm for realtime multivariate optimization
Hill, D.N., Nassif, H., Liu, Y., Iyer, A., and Vishwanathan, S.V.N · 2017
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Estimating individual treatment effect: generalization bounds and algorithms
Shalit, U., Johansson, F., and Sontag, D · 2017
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