每日简报

对称地拟合数据中的方程关系AI 解读

We consider the problem of fitting a relationship (e.g. a potential scientific law) to data involving multiple variables. Ordinary (least squares) regression is not suitable for this because the estimated relationship will differ according to which variable is chosen as being dependent, and the dependent variable is unrealistically assumed to be the only variable which has any measurement error (noise). We present a very general method for estimating a linear functional…

双重机器学习与面板数据：潜力、陷阱及可能的解决方案AI 解读

Estimating causal effect using machine learning (ML) algorithms can help to relax functional form assumptions if used within appropriate frameworks. However, most of these frameworks assume settings with cross-sectional data, whereas researchers often have access to panel data, which in traditional methods helps to deal with unobserved heterogeneity between units. In this paper, we explore how we can adapt double/debiased machine learning (DML) (Chernozhukov et al., 2018)…

大规模双重机器学习：预测客户行为的因果影响AI 解读

Causal Impact (CI) of customer actions are broadly used across the industry to inform both short- and long-term investment decisions of various types. In this paper, we apply the double machine learning (DML) methodology to estimate the CI values across 100s of customer actions of business interest and 100s of millions of customers. We operationalize DML through a causal ML library based on Spark with a flexible, JSON-driven model configuration approach to estimate CI at…

超越任意重复次数：因果推断模拟设计的原则性方法AI 解读

Evaluation of novel treatment effect estimators frequently relies on simulation studies lacking formal statistical comparisons and using arbitrary numbers of replications ($J$). This hinders reproducibility and efficiency. We propose the Test-Informed Simulation Count Algorithm (TISCA) to address these shortcomings. TISCA integrates Welch's t-tests with power analysis, iteratively running simulations until a pre-specified power (e.g., 0.8) is achieved for detecting a…

高维分位数预测的稀疏 PAC-Bayes 方法AI 解读

Quantile regression, a robust method for estimating conditional quantiles, has advanced significantly in fields such as econometrics, statistics, and machine learning. In high-dimensional settings, where the number of covariates exceeds sample size, penalized methods like lasso have been developed to address sparsity challenges. Bayesian methods, initially connected to quantile regression via the asymmetric Laplace likelihood, have also evolved, though issues with posterior…

存在长期存活者时基于目标量的推断AI 解读

In this article, we develop nonparametric inference methods for comparing survival data across two samples, which are beneficial for clinical trials of novel cancer therapies where long-term survival is a critical outcome. These therapies, including immunotherapies or other advanced treatments, aim to establish durable effects. They often exhibit distinct survival patterns such as crossing or delayed separation and potentially leveling-off at the tails of survival curves,…

疾病预防的成本效果分析：结直肠癌筛查案例AI 解读

Cancer Screening has been widely recognized as an effective strategy for preventing the disease. Despite its effectiveness, determining when to start screening is complicated, because starting too early increases the number of screenings over lifetime and thus costs but starting too late may miss the cancer that could have been prevented. Therefore, to make an informed recommendation on the age to start screening, it is necessary to conduct cost-effectiveness analysis to…

异质性时间序列的惩罚化子群划分AI 解读

Interest in the study and analysis of dynamic processes in the social, behavioral, and health sciences has burgeoned in recent years due to the increased availability of intensive longitudinal data. However, how best to model and account for the persistent heterogeneity characterizing such processes remains an open question. The multi-VAR framework, a recent methodological development built on the vector autoregressive model, accommodates heterogeneous dynamics in…

以加权多重检验调整后的 p 值辅助领域专家共建因果图AI 解读

This paper proposes an approach facilitating co-design of causal graphs between subject matter experts and statistical modellers. Modern causal analysis starting with formulation of causal graphs provides benefits for robust analysis and well-grounded decision support. Moreover, this process can enrich the discovery and planning phase of data science projects. The key premise is that plotting relevant statistical information on a causal graph structure can facilitate an…

使用 R 包 panelPomp 分析部分可观测马尔可夫面板过程的教程AI 解读

The R package panelPomp supports analysis of panel data via a general class of partially observed Markov process models (PanelPOMP). This package tutorial describes how the mathematical concept of a PanelPOMP is represented in the software and demonstrates typical use-cases of panelPomp. Monte Carlo methods used for POMP models require adaptation for PanelPOMP models due to the higher dimensionality of panel data. The package takes advantage of recent advances for PanelPOMP,…

内生性下的极端分位数处理效应：评估最弱势群体的政策效果AI 解读

We introduce a novel method for estimating and conducting inference about extreme quantile treatment effects (QTEs) in the presence of endogeneity. Our approach is applicable to a broad range of empirical research designs, including instrumental variables design and regression discontinuity design, among others. By leveraging regular variation and subsampling, the method ensures robust performance even in extreme tails, where data may be sparse or entirely absent. Simulation…

利用外部数据分析随机临床试验：迁移学习方法AI 解读

Randomized clinical trials are the gold standard for analyzing treatment effects, but high costs and ethical concerns can limit recruitment, potentially leading to invalid inferences. Incorporating external trial data with similar characteristics into the analysis using transfer learning appears promising for addressing these issues. In this paper, we present a formal framework for applying transfer learning to the analysis of clinical trials, considering three key…

自然实验估计器的基准评测：新数据集与双重稳健算法AI 解读

Estimating the effect of treatments from natural experiments, where treatments are pre-assigned, is an important and well-studied problem. We introduce a novel natural experiment dataset obtained from an early childhood literacy nonprofit. Surprisingly, applying over 20 established estimators to the dataset produces inconsistent results in evaluating the nonprofit's efficacy. To address this, we create a benchmark to evaluate estimator accuracy using synthetic outcomes,…

序贯搜寻模型的 MPEC 估计器AI 解读

This paper proposes a constrained maximum likelihood estimator for sequential search models, using the MPEC (Mathematical Programming with Equilibrium Constraints) approach. This method enhances numerical accuracy while avoiding ad hoc components and errors related to equilibrium conditions. Monte Carlo simulations show that the estimator performs better in small samples, with lower bias and root-mean-squared error, though less effectively in large samples. Despite these…

具有网络结构与非对称性的时空整数值数据波动建模AI 解读

This paper proposes a spatial threshold GARCH-type model for dynamic spatio-temporal integer-valued data with network structure. The proposed model can simplify the parameterization by using network structure in data, and can capture the asymmetric property in dynamic volatility by adopting a threshold structure. The proposed model assumes the conditional distribution is Poisson distribution. Asymptotic theory of maximum likelihood estimation (MLE) for the spatial model is…

处理效应模型中的空间干扰检测AI 解读

Modeling the interference effect is an important issue in the field of causal inference. Existing studies rely on explicit and often homogeneous assumptions regarding interference structures. In this paper, we introduce a low-rank and sparse treatment effect model that leverages data-driven techniques to identify the locations of interference effects. A profiling algorithm is proposed to estimate the model coefficients, and based on these estimates, global test and local…

通过倾向得分校准改善双重／去偏机器学习的平均处理效应估计AI 解读

In the last decade, machine learning techniques have gained popularity for estimating causal effects. One machine learning approach that can be used for estimating an average treatment effect is Double/debiased machine learning (DML) (Chernozhukov et al., 2018). This approach uses a double-robust score function that relies on the prediction of nuisance functions, such as the propensity score, which is the probability of treatment assignment conditional on covariates.…

存在多个事件的双重差分AI 解读

This paper studies staggered Difference-in-Differences (DiD) design when there is a second event confounding the target event. When the events are correlated, the treatment and the control group are unevenly exposed to the effects of the second event, causing an omitted event bias. To address this bias, I propose a two-stage DiD design. In the first stage, I estimate the combined effects of both treatments using a control group that is neither treated nor confounded. In the…

多层样本选择下的 Horowitz–Manski–Lee 界限AI 解读

This paper investigates the causal effect of job training on wage rates in the presence of firm heterogeneity. When training affects the sorting of workers to firms, sample selection is no longer binary but is ``multilayered". This paper extends the canonical Heckman (1979) sample selection model -- which assumes selection is binary -- to a setting where it is multilayered. In this setting Lee bounds set identifies a total effect that combines a weighted-average of the…

病例构成变化下预测性能的因果视角：预后与诊断的区分度和校准度AI 解读

Prediction models need reliable predictive performance as they inform clinical decisions, aiding in diagnosis, prognosis, and treatment planning. The predictive performance of these models is typically assessed through discrimination and calibration. Changes in the distribution of the data impact model performance and there may be important changes between a model's current application and when and where its performance was last evaluated. In health-care, a typical change is…

隐变量 DAG 中的平均因果效应估计：超越后门与前门准则AI 解读

The identification theory for causal effects in directed acyclic graphs (DAGs) with hidden variables is well established, but methods for estimating and inferring functionals that extend beyond the g-formula remain underdeveloped. Previous studies have introduced semiparametric estimators for such functionals in a broad class of DAGs with hidden variables. While these estimators exhibit desirable statistical properties such as double robustness in certain cases, they also…

凸非参数最小二乘的结构化 Lasso：瑞典配电网络应用AI 解读

We study the problem of variable selection in convex nonparametric least squares (CNLS). Whereas the least absolute shrinkage and selection operator (Lasso) is a popular technique for least squares, its variable selection performance is unknown in CNLS problems. In this work, we investigate the performance of the Lasso estimator and find out it is usually unable to select variables efficiently. Exploiting the unique structure of the subgradients in CNLS, we develop a…

加速因子的因果解释AI 解读

In studies of time-to-event outcomes with unmeasured heterogeneity, the hazard ratio for treatment is known to have a complex causal interpretation. Accelerated failure time (AFT) models, which assess the effect on the survival time ratio scale, are often suggested as a better alternative because they model a parameter with direct causal interpretation while allowing straightforward adjustment for measured confounders. In this work, we formalize the causal interpretation of…

单变量与双变量结局的简便分布双重差分估计量AI 解读

We provide a simple distribution regression estimator for treatment effects in the difference-in-differences (DiD) design. Our procedure is particularly useful when the treatment effect differs across the distribution of the outcome variable. Our proposed estimator easily incorporates covariates and, importantly, can be extended to settings where the treatment potentially affects the joint distribution of multiple outcomes. Our key identifying restriction is that the…