Causal Inference under Selection on Observables in Operations Management Research: Matching Methods and Synthetic Controls

Yılmaz, Övünç; Son, Yoonseock; Shang, Guangzhi; Arslan, Hayri Alper

doi:10.2139/ssrn.4310241

Cited by 1 publication

(2 citation statements)

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“…To overcome these issues, we obtain the operating data of 271 additional hotels in Shanghai with the permission of CLG's management team. This allows us to use the synthetic control method to evaluate the effectiveness of our approach (Yılmaz, Son, et al, 2022). This method identifies a high quality synthetic control hotel for each pilot hotel based on a weighted average of hotels whose trend closely matches that of each pilot hotel.…”

Section: Effectiveness Evaluation Using a Field Experimentsmentioning

confidence: 99%

“…With the approval of CLG's management team, we obtain the operating data of an additional 271 hotels in Shanghai, allowing us to use the synthetic control method to evaluate the effectiveness of our system (Abadie et al, 2010; Abadie & Gardeazabal, 2003; Ferreira & Mower, 2022; Yılmaz, Son, et al, 2022). In this method, for each treatment hotel, we find a high quality synthetic control hotel constructed from a weighted average of hotels whose trend closely matches the trend of the treatment hotel.…”

Section: Field Experimentsmentioning

confidence: 99%

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A reinforcement learning approach for hotel revenue management with evidence from field experiments

Chen

et al. 2023

J of Ops Management

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We consider a budget hotel chain's revenue management problem of deciding how to dynamically allocate capacity to multiple segments of customers. Our work solves an industrial‐sized problem faced by practitioners, with the reality of implementation motivating us to develop a tailored reinforcement learning approach. Our approach proceeds in two steps. First, a recommended average discount is computed with a reinforcement learning algorithm. Then, the recommended average discount is turned into a capacity allocation through a linear program. This approach overcomes the challenges of characterizing demand and estimating cancellations, and it facilitates hotel managers' acceptance of the revenue management system. We implement this approach in the hotel chain in a pilot study and assess its effectiveness using synthetic control methods. Our approach improves the key operational performance measure—revenue per available room—by 11.80%. There is heterogeneity in how the pilot hotels improve their revenue per available room. Some mainly increase their occupancy rate, some mainly increase the average daily room rate, while others experience significant increases in both. Further analysis shows that our approach uncovers the individual sources of suboptimal performance in pilot hotels and correspondingly improves decision‐making. Our work demonstrates that a reinforcement learning approach for hotel revenue management is promising.

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Section: Effectiveness Evaluation Using a Field Experimentsmentioning

confidence: 99%