供应商和零售商之间定价关系的因果关系分析M
Causal Analysis of Pricing Relationship between Suppliers and Retailers
摘要:
本文考虑使用两种机器学习算法来识别零售价格、供应商价格和销售量之间的因果关系，目的是找到零售和供应商之间动态博弈下的定价权。本文使用最早期的PC算法和LiNGAM (线性非高斯无环模型)。所研究的数据集为某地区大型零售超市的食品产品的数据。PC算法不能在三个节点，即零售价格、供应商价格和销售数量之间找到因果方向，而基于非高斯分布误差数据下的LiNGAM算法能够找到三节点下的因果方向。
Abstract:
In this paper, two machine learning algorithms are used to identify the causal relationship among retail price, supplier price and sales volume in order to find the pricing power under the dynamic game between retail and supplier. This paper uses the earliest PC algorithm and lingam (linear non-Gaussian acyclic model). The data set studied is the data of food products of a large retail supermarket in a certain area. PC algorithm cannot find the causal direction among three nodes, namely retail price, supplier price and sales quantity, while lingam algorithm based on non Gaussian error data can find the causal direction under three nodes.
参考文献
|
[1]
|
Pearl, J. (2009) Causality: Models, Reasoning, and Inference. 2nd edition, Cambridge University Press, Los Angeles. [Google Scholar] [CrossRef]
|
|
[2]
|
Shimizu, S., Hyvärinen, A., Kano, Y. and Hoyer, P.O. (2005) Discovery of Non-Gaussian Linear Causal Models Using ICA. Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI-2005), Quebec. non-Gaussianity.
|
|
[3]
|
Hyvärinen, A., Zhang, K., Shimizu, S. and Hoyer, P.O. (2010). Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity. Journal of Machine Learning Research, 11, 1709-1731.
|
|
[4]
|
Spirtes, P. (2010) Introduction to Causal Inference. The Journal of Machine Learning Research, 99, 1643-1662.
|
|
[5]
|
Stone, J.V. (2004) Independent Component Analysis: A Tutorial Introduction. Cambridge, MA: MIT Press. [Google Scholar] [CrossRef]
|