mlsanityData to 13 Oct 2026

Causal Customer Churn Analysis with Low-rank Tensor Block Hazard Model

arXiv 2405.11377 ↗First posted 18 May 2024stat.ML cs.LG stat.MEcited by 2

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Abstract (arXiv)

This study introduces an innovative method for analyzing the impact of various interventions on customer churn, using the potential outcomes framework. We present a new causal model, the tensorized latent factor block hazard model, which incorporates tensor completion methods for a principled causal analysis of customer churn. A crucial element of our approach is the formulation of a 1-bit tensor completion for the parameter tensor. This captures hidden customer characteristics and temporal elements from churn records, effectively addressing the binary nature of churn data and its time-monotonic trends. Our model also uniquely categorizes interventions by their similar impacts, enhancing the precision and practicality of implementing customer retention strategies. For computational efficiency, we apply a projected gradient descent algorithm combined with spectral clustering. We lay down the theoretical groundwork for our model, including its non-asymptotic properties. The efficacy and superiority of our model are further validated through comprehensive experiments on both simulated and real-world applications.

Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Builds on 23 of its 69 references that are in the map. First posted on arXiv 18 May 2024.

Builds on

23 of 69 references.

References · 23

  1. 2016DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Networkcited by 356
  2. 2021Causal Matrix Completioncited by 45
  3. 2019Nonconvex Low-Rank Tensor Completion from Noisy Datacited by 35
  4. 2017On Polynomial Time Methods for Exact Low Rank Tensor Completioncited by 27
  5. 2020Double/Debiased Machine Learning for Dynamic Treatment Effects via g-Estimationcited by 27
  6. 2013A Max-Norm Constrained Minimization Approach to 1-Bit Matrix Completioncited by 25
  7. 2020Synthetic Interventionscited by 25
  8. 2017Statistically Optimal and Computationally Efficient Low Rank Tensor Completion from Noisy Entriescited by 23
  9. 2020Exact Clustering in Tensor Block Model: Statistical Optimality and Computational Limitcited by 23
  10. 2019Iterative Algorithm for Discrete Structure Recoverycited by 21
  11. 2018Learning from Binary Multiway Data: Probabilistic Tensor Decomposition and its Statistical Optimalitycited by 15
  12. 2019DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Valuescited by 15
11 more
  1. 2018Learning tensors from partial binary measurementscited by 13
  2. 2018Robust Estimation of Causal Effects via High-Dimensional Covariate Balancing Propensity Scorecited by 12
  3. 2020Improving trial generalizability using observational studiescited by 7
  4. 2022Doubly robust estimators for generalizing treatment effects on survival outcomes from randomized controlled trials to a target populationcited by 7
  5. 2015Propensity Score Matching and Subclassification in Observational Studies with Multi-level Treatmentscited by 6
  6. 2021Covariate-assisted Sparse Tensor Completioncited by 4
  7. 2023Causal Analysis of Customer Churn Using Deep Learningcited by 3
  8. 2024Mixed Matrix Completion in Complex Survey Sampling under Heterogeneous Missingnesscited by 2
  9. 2018Semiparametric estimation of structural failure time model in continuous-time processescited by 1
  10. 2019Weighted Tensor Completion for Time-Series Causal Inferencecited by 1
  11. 2022Synthetic Blips: Generalizing Synthetic Controls for Dynamic Treatment Effectscited by 1

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Citing papers per year: 2025: 2.

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  2. 2025Synthetic Survival Control: Extending Synthetic Controls for "When-If" Decisioncited by 0

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