Pricing Experimental Design: Causal Effect, Expected Revenue and Tail Risk

David Simchi-Levi, Chonghuan Wang
Proceedings of the 40th International Conference on Machine Learning, PMLR 202:31788-31799, 2023.

Abstract

When launching a new product, historical sales data is often not available, leaving price as a crucial experimental instrument for sellers to gauge market response. When designing pricing experiments, there are three fundamental objectives: estimating the causal effect of price (i.e., price elasticity), maximizing the expected revenue through the experiment, and controlling the tail risk suffering from a very huge loss. In this paper, we reveal the relationship among such three objectives. Under a linear structural model, we investigate the trade-offs between causal inference and expected revenue maximization, as well as between expected revenue maximization and tail risk control. Furthermore, we propose an optimal pricing experimental design, which can flexibly adapt to different desired levels of trade-offs. Through the optimal design, we also explore the relationship between causal inference and tail risk control.

Cite this Paper

BibTeX
@InProceedings{pmlr-v202-simchi-levi23a, title = {Pricing Experimental Design: Causal Effect, Expected Revenue and Tail Risk}, author = {Simchi-Levi, David and Wang, Chonghuan}, booktitle = {Proceedings of the 40th International Conference on Machine Learning}, pages = {31788--31799}, year = {2023}, editor = {Krause, Andreas and Brunskill, Emma and Cho, Kyunghyun and Engelhardt, Barbara and Sabato, Sivan and Scarlett, Jonathan}, volume = {202}, series = {Proceedings of Machine Learning Research}, month = {23--29 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v202/simchi-levi23a/simchi-levi23a.pdf}, url = {https://proceedings.mlr.press/v202/simchi-levi23a.html}, abstract = {When launching a new product, historical sales data is often not available, leaving price as a crucial experimental instrument for sellers to gauge market response. When designing pricing experiments, there are three fundamental objectives: estimating the causal effect of price (i.e., price elasticity), maximizing the expected revenue through the experiment, and controlling the tail risk suffering from a very huge loss. In this paper, we reveal the relationship among such three objectives. Under a linear structural model, we investigate the trade-offs between causal inference and expected revenue maximization, as well as between expected revenue maximization and tail risk control. Furthermore, we propose an optimal pricing experimental design, which can flexibly adapt to different desired levels of trade-offs. Through the optimal design, we also explore the relationship between causal inference and tail risk control.} }
Endnote
%0 Conference Paper %T Pricing Experimental Design: Causal Effect, Expected Revenue and Tail Risk %A David Simchi-Levi %A Chonghuan Wang %B Proceedings of the 40th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2023 %E Andreas Krause %E Emma Brunskill %E Kyunghyun Cho %E Barbara Engelhardt %E Sivan Sabato %E Jonathan Scarlett %F pmlr-v202-simchi-levi23a %I PMLR %P 31788--31799 %U https://proceedings.mlr.press/v202/simchi-levi23a.html %V 202 %X When launching a new product, historical sales data is often not available, leaving price as a crucial experimental instrument for sellers to gauge market response. When designing pricing experiments, there are three fundamental objectives: estimating the causal effect of price (i.e., price elasticity), maximizing the expected revenue through the experiment, and controlling the tail risk suffering from a very huge loss. In this paper, we reveal the relationship among such three objectives. Under a linear structural model, we investigate the trade-offs between causal inference and expected revenue maximization, as well as between expected revenue maximization and tail risk control. Furthermore, we propose an optimal pricing experimental design, which can flexibly adapt to different desired levels of trade-offs. Through the optimal design, we also explore the relationship between causal inference and tail risk control.
APA
Simchi-Levi, D. & Wang, C.. (2023). Pricing Experimental Design: Causal Effect, Expected Revenue and Tail Risk. Proceedings of the 40th International Conference on Machine Learning, in Proceedings of Machine Learning Research 202:31788-31799 Available from https://proceedings.mlr.press/v202/simchi-levi23a.html.

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