Augmented Learning of Heterogeneous Treatment Effects via Gradient Boosting Trees

动态正则化CBDT：用于可解释异质处理效应的方差校准因果提升

Dynamic Regularized CBDT: Variance-Calibrated Causal Boosting for Interpretable Heterogeneous Treatment Effects

针对基于树的因果推理技术在观测数据中估计误差高的问题，提出动态正则化因果提升决策树（CBDT）框架，通过整合方差正则化与平均处理效应校准、动态更新正则化参数，显著提升估计精度并保持可解释性。

Quasi-Oracle Estimation of Heterogeneous Treatment Effects

Flexible estimation of heterogeneous treatment effects lies at the heart of many statistical challenges, such as personalized medicine and optimal resource allocation. In this paper, we develop a general class of two-step algorithms for heterogeneous treatment effect estimation in observational studies. We first estimate marginal effects and treatment propensities in order to form an objective function that isolates the causal component of the signal. Then, we optimize this data-adaptive objective function. Our approach has several advantages over existing methods. From a practical perspective…

Boosting Differentiable Causal Discovery via Adaptive Sample Reweighting

Under stringent model type and variable distribution assumptions, differentiable score-based causal discovery methods learn a directed acyclic graph (DAG) from observational data by evaluating candidate graphs over an average score function. Despite great success in low-dimensional linear systems, it has been observed that these approaches overly exploit easier-to-fit samples, thus inevitably learning spurious edges. Worse still, inherent mostly in these methods the common homogeneity assumption can be easily violated, due to the widespread existence of heterogeneous data in the real world, re…

SnapBoost: A Heterogeneous Boosting Machine

Modern gradient boosting software frameworks, such as XGBoost and LightGBM, implement Newton descent in a functional space. At each boosting iteration, their goal is to find the base hypothesis, selected from some base hypothesis class, that is closest to the Newton descent direction in a Euclidean sense. Typically, the base hypothesis class is fixed to be all binary decision trees up to a given depth. In this work, we study a Heterogeneous Newton Boosting Machine (HNBM) in which the base hypothesis class may vary across boosting iterations. Specifically, at each boosting iteration, the base h…

Ricci-Filtration：通过离散Ricci流提升检索增强生成重排序器在查询-答案任务中的性能

Ricci-Filtration: Boosting Retrieval-Augmented Generation Reranker to Query-Answer Tasks by Discrete Ricci Flow

提出基于离散曲率和Ricci流的几何重排序增强方法Ricci-Filtration，通过建模查询与检索块为网络并利用曲率过滤噪声块，显著提升RAG生成性能。

Gradient Boosting Survival Tree with Applications in Credit Scoring

Credit scoring plays a vital role in the field of consumer finance. Survival analysis provides an advanced solution to the credit-scoring problem by quantifying the probability of survival time. In order to deal with highly heterogeneous industrial data collected in Chinese market of consumer finance, we propose a nonparametric ensemble tree model called gradient boosting survival tree (GBST) that extends the survival tree models with a gradient boosting algorithm. The survival tree ensemble is learned by minimizing the negative log-likelihood in an additive manner. The proposed model optimize…