Multiple treatment effect estimation for business analytics using observational data

Abstract

To correctly evaluate the effects of treatments, conducting randomized controlled trials (RCTs) is a reasonable approach. However, because it is generally difficult to implement RCTs for all treatments, methods to estimate the treatment effects using observational data have been actively studied and used in various decision-making processes. Observational data accumulated in business activities and elsewhere contains the results of various previously implemented treatments, and correctly estimating the effects of any given treatment without separating the impacts of other treatments is challenging. Against this background, this paper proposes a method to estimate the effects of multiple treatments of various types while considering various causal relationships. Specifically, the proposal is a variational inference method that estimates the effect of multiple treatments using four latent factors estimated from observations, making assumptions that are independent of the type and number of treatments. The proposed method makes it possible to appropriately estimate the effects of measures even in situations with complex causal relationships. In addition, in situations where measures with continuous parameters are being implemented, it is possible to estimate the effects of measures that have not been implemented in the past by treating the content of the measures as a continuous variable.

Keywords:

• E-commerce marketing
• causal inference
• variational autoencoder
• machine learning
• business analytics
• treatment effect

REVIEWING EDITOR:

• Jenhui Chen, Chang Gung University, Taiwan

SUBJECTS:

• Internet & Multimedia - Computing & IT
• Artificial Intelligence; Computer Science (General)

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by JSPS KAKENHI (Grant Number 21H04600) and JST SPRING (Grant Number JPMJSP2128).