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极端分位数回归神经网络及其洪水风险预测应用AI 解读

Risk assessment for extreme events requires accurate estimation of high quantiles that go beyond the range of historical observations. When the risk depends on the values of observed predictors, regression techniques are used to interpolate in the predictor space. We propose the EQRN model that combines tools from neural networks and extreme value theory into a method capable of extrapolation in the presence of complex predictor dependence. Neural networks can naturally…

部分聚类随机试验中的因果推断AI 解读

Clustering and dependence are common in trials. For example, in some cluster randomized trials (CRTs), pre-existing clusters are enrolled, randomized, and serve as the basis of intervention delivery. Such CRTs are "fully clustered": participants are dependent within clusters. In contrast, "partially clustered" trials contain a mix of participants that are dependent within clusters and participants that are completely independent. One example of this design is a trial where…

海湾国家的经济多元化与社会进步：从石油依赖到知识经济AI 解读

The Gulf Cooperation Council countries -- Oman, Bahrain, Kuwait, UAE, Qatar, and Saudi Arabia -- holds strategic significance due to its large oil reserves. However, these nations face considerable challenges in shifting from oil-dependent economies to more diversified, knowledge-based systems. This study examines the progress of Gulf Cooperation Council (GCC) countries in achieving economic diversification and social development, focusing on the Social Progress Index (SPI),…

海湾合作委员会国家政治稳定性的预测AI 解读

Political stability is crucial for the socioeconomic development of nations, particularly in geopolitically sensitive regions such as the Gulf Cooperation Council Countries, Saudi Arabia, UAE, Kuwait, Qatar, Oman, and Bahrain. This study focuses on predicting the political stability index for these six countries using machine learning techniques. The study uses data from the World Banks comprehensive dataset, comprising 266 indicators covering economic, political, social,…

处理效应与因果参数的双重／去偏机器学习AI 解读

Most modern supervised statistical/machine learning (ML) methods are explicitly designed to solve prediction problems very well. Achieving this goal does not imply that these methods automatically deliver good estimators of causal parameters. Examples of such parameters include individual regression coefficients, average treatment effects, average lifts, and demand or supply elasticities. In fact, estimates of such causal parameters obtained via naively plugging ML…

利用伪值随机森林建模限制平均生存时间AI 解读

The restricted mean survival time (RMST) has become a popular measure to summarize event times in longitudinal studies. Defined as the area under the survival function up to a time horizon $τ$ > 0, the RMST can be interpreted as the life expectancy within the time interval [0, $τ$]. In addition to its straightforward interpretation, the RMST also allows for the definition of valid estimands for the causal analysis of treatment contrasts in medical studies. In this work, we…

利用条件生成模型对可识别因果干预分布进行采样AI 解读

Causal inference from observational data plays critical role in many applications in trustworthy machine learning. While sound and complete algorithms exist to compute causal effects, many of them assume access to conditional likelihoods, which is difficult to estimate for high-dimensional (particularly image) data. Researchers have alleviated this issue by simulating causal relations with neural models. However, when we have high-dimensional variables in the causal graph…

以稳健性为目标选择条件平均处理效应估计量AI 解读

The growing demand for personalized decision-making has led to a surge of interest in estimating the Conditional Average Treatment Effect (CATE). Various types of CATE estimators have been developed with advancements in machine learning and causal inference. However, selecting the desirable CATE estimator through a conventional model validation procedure remains impractical due to the absence of counterfactual outcomes in observational data. Existing approaches for CATE…

条件矩限制下的机器学习去偏：局部平均处理效应应用AI 解读

Models with Conditional Moment Restrictions (CMRs) are popular in economics. These models involve finite and infinite dimensional parameters. The infinite dimensional components include conditional expectations, conditional choice probabilities, or policy functions, which might be flexibly estimated using Machine Learning tools. This paper presents a characterization of locally debiased moments for regular models defined by general semiparametric CMRs with possibly different…

为实现人机互补而设计算法建议AI 解读

Algorithms frequently assist, rather than replace, human decision-makers. However, the design and analysis of algorithms often focus on predicting outcomes and do not explicitly model their effect on human decisions. This discrepancy between the design and role of algorithmic assistants becomes particularly concerning in light of empirical evidence that suggests that algorithmic assistants again and again fail to improve human decisions. In this article, we formalize the…

依赖数据下使用深度神经网络的部分线性模型推断AI 解读

I consider inference in a partially linear regression model under stationary $β$-mixing data after first stage deep neural network (DNN) estimation. Using the DNN results of Brown (2024), I show that the estimator for the finite dimensional parameter, constructed using DNN-estimated nuisance components, achieves $\sqrt{n}$-consistency and asymptotic normality. By avoiding sample splitting, I address one of the key challenges in applying machine learning techniques to…

对密度比估计误差稳健的双重去偏协变量漂移适应AI 解读

Consider a scenario where we have access to train data with both covariates and outcomes while test data only contains covariates. In this scenario, our primary aim is to predict the missing outcomes of the test data. With this objective in mind, we train parametric regression models under a covariate shift, where covariate distributions are different between the train and test data. For this problem, existing studies have proposed covariate shift adaptation via importance…

没有对照组的因果推断与政策评价AI 解读

Without a control group, the most widespread methodologies for estimating causal effects cannot be applied. To fill this gap, we propose the Machine Learning Control Method, a new approach for causal panel analysis that estimates causal parameters without relying on untreated units. We formalize identification within the potential outcomes framework and then provide estimation based on machine learning algorithms. To illustrate the practical relevance of our method, we…

新型处理效应估计模型的消融研究AI 解读

Ablation studies are essential for understanding the contribution of individual components within complex models, yet their application in nonparametric treatment effect estimation remains limited. This paper emphasizes the importance of ablation studies by examining the Bayesian Causal Forest (BCF) model, particularly the inclusion of the estimated propensity score $\hatπ(x_i)$ intended to mitigate regularization-induced confounding (RIC). Through a partial ablation study…

神经网络菲利普斯曲线与深度产出缺口AI 解读

Many problems plague empirical Phillips curves (PCs). Among them is the hurdle that the two key components, inflation expectations and the output gap, are both unobserved. Traditional remedies include proxying for the absentees or extracting them via assumptions-heavy filtering procedures. I propose an alternative route: a Hemisphere Neural Network (HNN) whose architecture yields a final layer where components can be interpreted as latent states within a Neural PC. There are…

函数极小极大优化中神经随机梯度下降上升的平均场分析AI 解读

This paper studies minimax optimization problems defined over infinite-dimensional function classes of overparameterized two-layer neural networks. In particular, we consider the minimax optimization problem stemming from estimating linear functional equations defined by conditional expectations, where the objective functions are quadratic in the functional spaces. We address (i) the convergence of the stochastic gradient descent-ascent algorithm and (ii) the representation…

未观测混杂下的因果公平性：神经网络敏感性框架AI 解读

Fairness for machine learning predictions is widely required in practice for legal, ethical, and societal reasons. Existing work typically focuses on settings without unobserved confounding, even though unobserved confounding can lead to severe violations of causal fairness and, thus, unfair predictions. In this work, we analyze the sensitivity of causal fairness to unobserved confounding. Our contributions are three-fold. First, we derive bounds for causal fairness metrics…

基于结构因果关系的可泛化概念发现模型AI 解读

The rising need for explainable deep neural network architectures has utilized semantic concepts as explainable units. Several approaches utilizing disentangled representation learning estimate the generative factors and utilize them as concepts for explaining DNNs. However, even though the generative factors for a dataset remain fixed, concepts are not fixed entities and vary based on downstream tasks. In this paper, we propose a disentanglement mechanism utilizing a…

基于异质环境优化的因果估计AI 解读

This paper presents a new optimization approach to causal estimation. Given data that contains covariates and an outcome, which covariates are causes of the outcome, and what is the strength of the causality? In classical machine learning (ML), the goal of optimization is to maximize predictive accuracy. However, some covariates might exhibit a non-causal association with the outcome. Such spurious associations provide predictive power for classical ML, but they prevent us…

利用多个工具变量识别离散、有序及连续处理的因果效应AI 解读

Inferring causal relationships from observational data is often challenging due to endogeneity. This paper provides new identification results for causal effects of discrete, ordered and continuous treatments using multiple binary instruments. The key contribution is the identification of a new causal parameter that has a straightforward interpretation with a positive weighting scheme and is applicable in many settings due to a mild monotonicity assumption. This paper…

异质性处理效应的数据驱动估计AI 解读

Estimating how a treatment affects different individuals, known as heterogeneous treatment effect estimation, is an important problem in empirical sciences. In the last few years, there has been a considerable interest in adapting machine learning algorithms to the problem of estimating heterogeneous effects from observational and experimental data. However, these algorithms often make strong assumptions about the observed features in the data and ignore the structure of the…

Multi-CATE：对未知协变量分布变化稳健的多重准确条件平均处理效应估计AI 解读

Estimating heterogeneous treatment effects is important to tailor treatments to those individuals who would most likely benefit. However, conditional average treatment effect predictors may often be trained on one population but possibly deployed on different, possibly unknown populations. We use methodology for learning multi-accurate predictors to post-process CATE T-learners (differenced regressions) to become robust to unknown covariate shifts at the time of deployment.…

降低凸回归中的过拟合AI 解读

Convex regression is a method for estimating the convex function from a data set. This method has played an important role in operations research, economics, machine learning, and many other areas. However, it has been empirically observed that convex regression produces inconsistent estimates of convex functions and extremely large subgradients near the boundary as the sample size increases. In this paper, we provide theoretical evidence of this overfitting behavior. To…

空间混杂的因果推断框架AI 解读

Over the past few decades, addressing "spatial confounding" has become a major topic in spatial statistics. However, the literature has provided conflicting definitions, and many proposed solutions are tied to specific analysis models and do not address the issue of confounding as it is understood in causal inference. We offer an analysis-model-agnostic definition of spatial confounding as the existence of an unmeasured causal confounder variable with a spatial structure. We…