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

arXiv: https://arxiv.org/abs/2405.11377 · HTML: https://mlsanity.com/paper/2405.11377/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 69 references that are in the map.
- First posted on arXiv 18 May 2024.

## Builds on (most cited first, 23 of 23)

- [DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network](https://mlsanity.com/paper/1606.00931/) (2016)
- [Causal Matrix Completion](https://mlsanity.com/paper/2109.15154/) (2021)
- [Nonconvex Low-Rank Tensor Completion from Noisy Data](https://mlsanity.com/paper/1911.04436/) (2019)
- [On Polynomial Time Methods for Exact Low Rank Tensor Completion](https://mlsanity.com/paper/1702.06980/) (2017)
- [Double/Debiased Machine Learning for Dynamic Treatment Effects via g-Estimation](https://mlsanity.com/paper/2002.07285/) (2020)
- [Synthetic Interventions](https://mlsanity.com/paper/2006.07691/) (2020)
- [A Max-Norm Constrained Minimization Approach to 1-Bit Matrix Completion](https://mlsanity.com/paper/1309.6013/) (2013)
- [Statistically Optimal and Computationally Efficient Low Rank Tensor Completion from Noisy Entries](https://mlsanity.com/paper/1711.04934/) (2017)
- [Exact Clustering in Tensor Block Model: Statistical Optimality and Computational Limit](https://mlsanity.com/paper/2012.09996/) (2020)
- [Iterative Algorithm for Discrete Structure Recovery](https://mlsanity.com/paper/1911.01018/) (2019)
- [Learning from Binary Multiway Data: Probabilistic Tensor Decomposition and its Statistical Optimality](https://mlsanity.com/paper/1811.05076/) (2018)
- [DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values](https://mlsanity.com/paper/1908.02337/) (2019)
- [Learning tensors from partial binary measurements](https://mlsanity.com/paper/1804.00108/) (2018)
- [Robust Estimation of Causal Effects via High-Dimensional Covariate Balancing Propensity Score](https://mlsanity.com/paper/1812.08683/) (2018)
- [Improving trial generalizability using observational studies](https://mlsanity.com/paper/2003.01242/) (2020)
- [Doubly robust estimators for generalizing treatment effects on survival outcomes from randomized controlled trials to a target population](https://mlsanity.com/paper/2201.06595/) (2022)
- [Propensity Score Matching and Subclassification in Observational Studies with Multi-level Treatments](https://mlsanity.com/paper/1508.06948/) (2015)
- [Covariate-assisted Sparse Tensor Completion](https://mlsanity.com/paper/2103.06428/) (2021)
- [Causal Analysis of Customer Churn Using Deep Learning](https://mlsanity.com/paper/2304.10604/) (2023)
- [Weighted Tensor Completion for Time-Series Causal Inference](https://mlsanity.com/paper/1902.04646/) (2019)
- [Mixed Matrix Completion in Complex Survey Sampling under Heterogeneous Missingness](https://mlsanity.com/paper/2402.03954/) (2024)
- [Semiparametric estimation of structural failure time model in continuous-time processes](https://mlsanity.com/paper/1808.06408/) (2018)
- [Synthetic Blips: Generalizing Synthetic Controls for Dynamic Treatment Effects](https://mlsanity.com/paper/2210.11003/) (2022)

## Built on by (most cited first, 2 of 2)

- [Doubly Robust Fusion of Many Treatments for Policy Learning](https://mlsanity.com/paper/2505.08092/) (2025)
- [Synthetic Survival Control: Extending Synthetic Controls for "When-If" Decision](https://mlsanity.com/paper/2511.14133/) (2025)

## 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.