Proceedings of the Fifth International Workshop on Experimental Economics and Machine Learning (EEML 2019),Perm, Russia, September 26, 2019
Proceedings of the Fifth Workshop on Experimental Economics and Machine Learning
at the National Research Univeristy Higher School of Economics co-located with the Seventh International Conference on Applied Research in Economics (iCare7)
Today, treatment effect estimation at the individual level is a vital problem in many areas of science and business. For example, in marketing, estimates of the treatment effect are used to select the most efficient promo-mechanics; in medicine, individual treatment effects are used to determine the optimal dose of medication for each patient and so on. At the same time, the question on choosing the best method, i.e., the method that ensures the smallest predictive error (for instance, RMSE) or the highest total (average) value of the effect, remains open. Accordingly, in this paper we compare the effectiveness of machine learning methods for estimation of individual treatment effects. The comparison is performed on the Criteo Uplift Modeling Dataset. In this paper we show that the combination of the Logistic Regression method and the Difference Score method as well as Uplift Random Forest method provide the best correctness of Individual Treatment Effect prediction on the top 30% observations of the test dataset.
This paper is an empirical study of the changing nature of the dependence of fundamental factors on the stock market index, which is the trend identified earlier in the Russian stock market. We empirically test the impact of daily values of fundamental factors on the MOEX Russia Index from 2003 to 2018. The analysis of the ARIMA-GARCH (1,1) model with a rolling window reveals that the change in the power and direction of the influence of the fundamental factors on the Russian stock market persists. The Quandt-Andrews breakpoint test and Bai-Perron test identify the number and likely location of structural breaks. We find multiple breaks probably associated with the dramatic falls of the stock market index. The results of the regression models over the different regimes, defined by the structural breaks, can vary markedly over time. This research is of value in macroeconomic forecasting and in the investment strategy development
A multichannel attribution model based on gradient boost- ing over trees is proposed, which was compared with the state of the art models: bagged logistic regression, Markov chains approach, shapely value. Experiments on digital advertising datasets showed that the pro- posed model is better than the solutions considered by ROC AUC metric. In addition, the problem of probability prediction of conversion by the consumer using the ensemble of the analyzed algorithms was solved, the meta-features obtained were enriched with consumers and offline activities of the advertising campaign data.
This paper discusses the scientific and practical perspectives of using general game playing in business-to-business price negotiations as a part of Procurement 4.0 revolution. The status quo of digital price negotiations software, which emerged from intuitive solutions to business goals and refereed to as electronic auctions in industry, is summarized in a scientific context. Description of such aspects as auctioneers’ interventions, asymmetry among players and time- depended features reveals the nature of nowadays electronic auctions to be rather termed as price games. This paper strongly suggests general game playing as the crucial technology for automation of human rule setting in those games. Game theory, genetic programming, experimental economics, and AI human player simulation are also discussed as satellite topics. SIDL-type game descriptions languages and their formal game-theoretic foundations are presented.