# Which Company Adjustment Matter? Insights from Uplift Modeling on Financial Health

arXiv: https://arxiv.org/abs/2506.19049 · HTML: https://mlsanity.com/paper/2506.19049/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 46 references that are in the map.
- First posted on arXiv 23 Jun 2025.

## Builds on (most cited first, 16 of 16)

- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://mlsanity.com/paper/1706.03461/) (2017)
- [CausalML: Python Package for Causal Machine Learning](https://mlsanity.com/paper/2002.11631/) (2020)
- [Nonparametric methods for doubly robust estimation of continuous treatment effects](https://mlsanity.com/paper/1507.00747/) (2015)
- [Policy Evaluation and Optimization with Continuous Treatments](https://mlsanity.com/paper/1802.06037/) (2018)
- [Identification and estimation of treatment and interference effects in observational studies on networks](https://mlsanity.com/paper/1609.06245/) (2016)
- [Uplift Modeling with Multiple Treatments and General Response Types](https://mlsanity.com/paper/1705.08492/) (2017)
- [A unified survey of treatment effect heterogeneity modeling and uplift modeling](https://mlsanity.com/paper/2007.12769/) (2020)
- [Estimation of causal effects with multiple treatments: a review and new ideas](https://mlsanity.com/paper/1701.05132/) (2017)
- [Uplift Modeling for Multiple Treatments with Cost Optimization](https://mlsanity.com/paper/1908.05372/) (2019)
- [Explicit Feature Interaction-aware Uplift Network for Online Marketing](https://mlsanity.com/paper/2306.00315/) (2023)
- [Learning to rank for uplift modeling](https://mlsanity.com/paper/2002.05897/) (2020)
- [NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments](https://mlsanity.com/paper/2103.11175/) (2021)
- [Uplift Modeling: from Causal Inference to Personalization](https://mlsanity.com/paper/2308.09066/) (2023)
- [A New Transformation Approach for Uplift Modeling with Binary Outcome](https://mlsanity.com/paper/2310.05549/) (2023)
- [Identifiability of causal effects with multiple causes and a binary outcome](https://mlsanity.com/paper/1907.13323/) (2019)
- [Augmenting Bankruptcy Prediction using Reported Behavior of Corporate Restructuring](https://mlsanity.com/paper/2401.14901/) (2024)

## Abstract (arXiv)

Uplift modeling has achieved significant success in various fields, particularly in online marketing. It is a method that primarily utilizes machine learning and deep learning to estimate individual treatment effects. This paper we apply uplift modeling to analyze the effect of company adjustment on their financial status, and we treat these adjustment as treatments or interventions in this study. Although there have been extensive studies and application regarding binary treatments, multiple treatments, and continuous treatments, company adjustment are often more complex than these scenarios, as they constitute a series of multiple time-dependent actions. The effect estimation of company adjustment needs to take into account not only individual treatment traits but also the temporal order of this series of treatments. This study collects a real-world data set about company financial statements and reported behavior in Luxembourg for the experiments. First, we use two meta-learners and three other well-known uplift models to analyze different company adjustment by simplifying the adjustment as binary treatments. Furthermore, we propose a new uplift modeling framework (MTDnet) to address the time-dependent nature of these adjustment, and the experimental result shows the necessity of considering the timing of these adjustment.