-
2023 Conference on Digital Experimentation @ MIT (CODE@MIT)2023There are many experimental settings that may suffer from cross-unit (customers, seller, advertiser, etc.) spillovers, for instance through network effects. Such effects introduce bias and prevent the experimenter from drawing trustworthy insights on the data. One approach to dealing with such spillovers is to group units into clusters and randomize treatment status at the cluster level. Examples of clusters
-
ESREL 20232023Enabling a circular economy aims to reduce the amount of global waste generated from electrical and electronic equipment, mitigate the associated risk to the ecosystem and human health, and address concerns over limited material resources. Durability is a critical concern because keeping products in use for a longer time should reduce resource consumption and waste. Assessing the durability of products
-
KDD 2023 Workshop on Causal Inference and Machine Learning in Practice: Use cases for Product, Brand, Policy and Beyond2023We introduce OpportunityFinder, a code-less framework for performing a variety of causal inference studies with panel data for non-expert users. In its current state, OpportunityFinder only requires users to provide raw observational data and a configuration file. A pipeline is then triggered that inspects/processes data, chooses the suitable algorithm(s) to execute the causal study. It returns the causal
-
Applied Marketing Analytics (AMA)2023Brands usually invest in a portfolio of digital ad products for brand consideration and conversion, and their performance is commonly evaluated with ad - attributed metrics. However, these metrics limit the measurement of advertising effectiveness within a short time window, typically of two weeks. Therefore, they could underestimate the total effect if some ad products' efficacy lasts beyond the measurement
-
KDD 2023 Workshop on Multi-Armed Bandits and Reinforcement Learning (MARBLE), ICML 2023 Workshop on The Many Facets of Preference-based Learning2023Motivated by bid recommendation in online ad auctions, this paper considers a general class of multi-level and multi-agent games, with two major characteristics: one is a large number of anonymous agents, and the other is the intricate interplay between competition and cooperation. To model such complex systems, we propose a novel and tractable bi-objective optimization formulation with mean-field approximation
Related content
-
March 21, 2024The principal economist and his team address unique challenges using techniques at the intersection of microeconomics, statistics, and machine learning.
-
October 10, 2023The system has expanded from generating peak computation-load forecasts one year in advance to a series of forecasts that include per-minute forecasts several months into the future.
-
October 05, 2023Wharton professor Jessie Handbury lends her expertise to Amazon’s PXTCS Team as an Amazon Visiting Academic.
-
September 19, 2023The new Fulfillment by Amazon system empowers sellers to have more transparency and control over their capacity within Amazon’s fullfilment network by applying market-based principles.
-
June 22, 2023Kanoria and coauthors honored for their paper narrowing the gap between theoretical understanding and practical experience in matching markets.
-
June 02, 2023In a plenary talk, the Berkeley professor and Distinguished Amazon Scholar will argue that AI research should borrow concepts from economics and focus on social collectives.