Prescriptive analytics in operations problems : a tree ensemble approach

The main contributions of this thesis concern addressing challenges in the field of prescriptive optimization, and how machine learning techniques can be incorporated into solving data-driven operational optimization problems. In chapter 2, we provide a data-driven study of the secondary ticket market. In particular we are primarily concerned with accurately estimating price sensitivity for listed tickets. We propose a semi-parametric model for measuring heterogeneous treatment effects using the concept of orthogonalization in the classification setting, and derive a novel loss function which can be solved using a range of off-the-shelf machine learning methods. Over a wide range of synthetic data experiments, we show how this approach beats state-of-the-art machine learning and causal inference methods for estimating treatment effects in classification tasks.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Operations Research Center

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Biggs, Max(Max Ray)

Advisor dc:contributor.advisor

• Georgia Perakis.

• Operations Research Center.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/123709

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/123709