An End-to-End Pipeline for Causal ML with Continuous Treatments: An Application to Financial Decision Making
连续处理因果机器学习的端到端流程：金融决策应用AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要直接研究连续处理的因果识别、效应估计、敏感性分析与政策优化。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
连续处理因果机器学习的端到端流程：金融决策应用
- 研究问题
- 面对高维数据和连续处理，如何把因果识别、正值性诊断、效应估计与政策优化整合成可实施的流程？
- 方法与思路
- 作者提出六步模块化流程，结合两阶段降维、连续处理正值性诊断，以及从二元处理迁移的估计与敏感性分析。
- 材料陈述的贡献
- 摘要称该流程补充了现有工具的实用组件，并在债务催收背景的合成数据上获得更高计算效率和更低估计偏差。
- 阅读关注点
- 应核对降维是否保留识别所需信息、正值性违反如何处理，以及模拟设计、比较基准和政策优化的评价标准。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实；摘要未说明具体识别条件，合成数据结果不能保证真实业务表现。
原文摘要
This paper presents an end-to-end causal machine learning (ML) pipeline designed for real-world applications with continuous treatments. The proposed framework consists of six sequential steps: dimensionality reduction, causal identification, positivity assumption violation handling, estimation, refutation and evaluation, and policy optimization. We introduce practical contributions not currently available in existing causal ML toolkits, specifically: (1) a method for detecting and quantifying positivity violations in continuous treatment settings (2) a novel, scalable two-stage dimensionality reduction framework tailored for causal inference with high-dimensional data; (3) the adaptation of sensitivity analysis and estimation methods originally designed for binary treatments to the continuous treatment space and (4) an end-to-end integration of these components into a modular, reproducible workflow. These innovations address real-world challenges in causal inference that are often not covered in theoretical frameworks but frequently encountered in industrial applications. The methodology is validated with a synthetic dataset inspired in a real-world financial debt collection use case, however its design can be applied to analogous problems across different industries. Results demonstrate that the proposed methodology offers a more computationally efficient approach and produces less biased estimates compared to standard methods for problems with continuous treatment and high-dimensional data. A fully functional GitHub repository with documented code and numbered notebooks is made available ensuring reproducibility and practical implementation. The pipeline presented is intended to contribute to closing the gap between academic approaches and practical application in industry contexts where causal ML can be highly beneficial such as the financial sector.