1,556 research outputs found
Uplift modeling using the transformed outcome approach
EPIA 2022. Conferência Internacional, realizada em Lisboa, Portugal, de 31 de agosto a 2 de setembro de 2022.Churn and how to deal with it is an essential issue in the telecommunications sector. Within the scope of actionable knowledge, we argue that it is crucial to find effective personalized interventions that can lead to a reduction in dropouts and that, at the same time, make it possible to determine the causal effect of these interventions. Considering an intervention that encourages clients to opt for a longer-term contract for benefits, we used Uplift modeling and the Transformed Outcome Approach as a machine learning-based technique for individual-level prediction. The result is actionable profiles of persuadable customers that increase retention and strike the right balance between the campaign budget.info:eu-repo/semantics/publishedVersio
The Best of Two Worlds – Using Recent Advances from Uplift Modeling and Heterogeneous Treatment Effects to Optimize Targeting Policies
The design of targeting policies is fundamental to address a variety of practical problems across a broad spectrum of domains from e-commerce to politics and medicine. Recently, researchers and practitioners have begun to predict individual treatment effects to optimize targeting policies. Although different research streams, that is, uplift modeling and heterogeneous treatment effect propose numerous methods to predict individual treatment effects, current approaches suffer from various practical challenges, such as weak model performance and a lack of reliability. In this study, we propose a new, tree- based, algorithm that combines recent advances from both research streams and demonstrate how its use can improve predicting the individual treatment effect. We benchmark our method empirically against state-of-the-art strategies and show that the proposed algorithm achieves excellent results. We demonstrate that our approach performs particularly well when targeting few customers, which is of paramount interest when designing targeting policies in a marketing context
The Best of Two Worlds – Using Recent Advances from Uplift Modeling and Heterogeneous Treatment Effects to Optimize Targeting Policies
The design of targeting policies is fundamental to address a variety of practical problems across a broad spectrum of domains from e-commerce to politics and medicine. Recently, researchers and practitioners have begun to predict individual treatment effects to optimize targeting policies. Although different research streams, that is, uplift modeling and heterogeneous treatment effect propose numerous methods to predict individual treatment effects, current approaches suffer from various practical challenges, such as weak model performance and a lack of reliability. In this study, we propose a new, tree- based, algorithm that combines recent advances from both research streams and demonstrate how its use can improve predicting the individual treatment effect. We benchmark our method empirically against state-of-the-art strategies and show that the proposed algorithm achieves excellent results. We demonstrate that our approach performs particularly well when targeting few customers, which is of paramount interest when designing targeting policies in a marketing context
Answering Layer 3 queries with DiscoSCMs
In the realm of causal inference, the primary frameworks are the Potential
Outcome (PO) and the Structural Causal Model (SCM), both predicated on the
consistency rule. However, when facing Layer 3 valuations, i.e., counterfactual
queries that inherently belong to individual-level semantics, they both seem
inadequate due to the issue of degeneration caused by the consistency rule. For
instance, in personalized incentive scenarios within the internet industry, the
probability of one particular user being a complier, denoted as , degenerates to a parameter that can only take values of 0 or 1. This
paper leverages the DiscoSCM framework to theoretically tackle the
aforementioned counterfactual degeneration problem, which is a novel framework
for causal modeling that combines the strengths of both PO and SCM, and could
be seen as an extension of them. The paper starts with a brief introduction to
the background of causal modeling frameworks. It then illustrates, through an
example, the difficulty in recovering counterfactual parameters from data
without imposing strong assumptions. Following this, we propose the DiscoSCM
with independent potential noise framework to address this problem.
Subsequently, the superior performance of the DiscoSCM framework in answering
counterfactual questions is demonstrated by several key results in the topic of
unit select problems. We then elucidate that this superiority stems from the
philosophy of individual causality. In conclusion, we suggest that DiscoSCM may
serve as a significant milestone in the causal modeling field for addressing
counterfactual queries
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