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Where to invest? ROI analysis with continuous treatments using doubly robust machine learning

Abstract Number:

3542 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Speed 

Participants:

Eray Turkel (1)

Institutions:

(1) Stanford University, N/A

First Author:

Eray Turkel  
Stanford University

Presenting Author:

Frank Yoon  
N/A

Abstract Text:

In sales operations, a business makes tradeoff decisions about where to invest and divest in order to optimize revenue. For instance, in advertising sales, a customer segment (e.g., by vertical) responds differently to treatments, such as the number of sales pitches about a new advertising platform. To optimize revenue impacts, the business needs to know where to make investments and divestments. We apply causal inference methods to estimate impacts of continuous treatments (i.e., dollars invested) and support tradeoff decision-making. Specifically, we implement doubly robust machine learning methods on observational sales data to (1) analyze sales treatment mechanisms and (2) estimate their impacts on revenue outcomes. Through simulation and real data analysis, we demonstrate the potential for doubly robust methods to mitigate bias in ROI decision-making in business problems about investments and resourcing.

Keywords:

Double robustness|ROI analysis|Machine learning|Advertising sales|Decision support|

Sponsors:

Business and Economic Statistics Section

Tracks:

Financial Econometrics

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I understand that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is non-refundable.

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