30 citations · 33 across the 6 of their papers we have counts for
7 papers
Beyond Demand Estimation: Consumer Surplus Evaluation via Cumulative Propensity Weights
Zeyu Bian, Max Biggs, Ruijiang Gao +1
This paper develops a practical framework for using observational data to audit the consumer surplus effects of AI-driven decisions, specifically in targeted pricing and algorithmi…
Post-estimation Adjustments in Data-driven Decision-making with Applications in Pricing
Michael Albert, Max Biggs, Ningyuan Chen +1
The predict-then-optimize (PTO) framework is a standard approach in data-driven decision-making, where a decision-maker first estimates an unknown parameter from historical data an…
Tight Mixed-Integer Optimization Formulations for Prescriptive Trees
Max Biggs, Georgia Perakis
We focus on modeling the relationship between an input feature vector and the predicted outcome of a trained decision tree using mixed-integer optimization. This can be used in man…
Convex Surrogate Loss Functions for Contextual Pricing with Transaction Data
Max Biggs
We study an off-policy contextual pricing problem where the seller has access to samples of prices that customers were previously offered, whether they purchased at that price, and…
Enhancing Counterfactual Classification via Self-Training
Ruijiang Gao, Max Biggs, Wei Sun +1
Unlike traditional supervised learning, in many settings only partial feedback is available. We may only observe outcomes for the chosen actions, but not the counterfactual outcome…
Loss Functions for Discrete Contextual Pricing with Observational Data
Max Biggs, Ruijiang Gao, Wei Sun
We study a pricing setting where each customer is offered a contextualized price based on customer and/or product features. Often only historical sales data are available, so we ob…
Model Distillation for Revenue Optimization: Interpretable Personalized Pricing
Max Biggs, Wei Sun, Markus Ettl
Data-driven pricing strategies are becoming increasingly common, where customers are offered a personalized price based on features that are predictive of their valuation of a prod…