每日简报

利用辅助外部数据识别治疗受益者的共形筛选框架AI 解读

Identifying patients who are likely to benefit from a treatment is central to precision medicine and can guide follow-up trials, enrichment designs, and individualized decisions. Although randomized controlled trials (RCTs) provide evidence on efficacy, they are usually powered to estimate average treatment effects rather than patient-level benefit. Meanwhile, artificial intelligence and machine learning methods offer flexible tools for estimating heterogeneous treatment…

数字孪生的相似性预测：面板数据、理论与应用AI 解读

Prediction from sequential panel data is central to digital-twin modeling, where new panels arrive over time and the predictive system is updated sequentially. Existing methods often rely on temporal proximity, which can fail when similar input-output patterns recur at nonadjacent times or when recent panels differ from the target panel. We propose State-Local Prediction (StaLoP), a nonparametric dynamic panel prediction framework that utilizes information through…

基于经验分位数的估计量的渐近性质AI 解读

We consider inference for parameters of the form $θ_0 = E[F_Y^{-1}\circ F_Z(X)]$ for some variables $X$, $Y$ and $Z$. Such parameters appear, in particular, in the ``changes-in-changes'' model of \cite{AtheyImbens2006}. We first establish that $\widehatθ$, a plug-in estimator of $θ_0$, is root-$n$ consistent and asymptotically normal under weaker conditions than those previously available, allowing in particular for unbounded variables. Next, we propose a new estimator of…

面向全体人群的因果推断：死亡截断结局的边际估计目标AI 解读

In longitudinal studies, outcomes of interest are often truncated by death, meaning that they are only observed or well-defined conditional on intercurrent events such as survival. Existing strategies face a trade-off: causally interpretable estimands, such as survivor average causal effects, target a latent subgroup, whereas while-alive and composite summaries apply to the full population but are difficult to interpret as causal effects on the non-mortality outcome. We…

结合分布方法的单侧与双侧混合数据风险差置信区间AI 解读

Combined unilateral and bilateral binary outcomes frequently arise in studies involving paired organs. The risk difference is a clinically interpretable measure for comparing treatment effects between groups. Existing confidence interval methods are primarily based on asymptotic normality and may fail to adequately reflect finite-sample distributional features, particularly skewness. To address this issue, we propose a distribution-based confidence interval derived from the…

真实世界证据中信息性治疗切换的工具变量方法AI 解读

Reproducible and generalizable assessment of treatment decisions requires principled handling of subsequent treatment switching that may inform expected outcomes and shift across cohorts and over time. To effectively account for informative treatment switching, we propose an instrumental variable approach that characterizes the poorly documented expected outcomes at switching as unmeasured confounding. After establishing the baseline treatment as a viable instrumental…

序贯实验的半参数效率：基于平均倾向得分的刻画与设计AI 解读

Modern experiments, including evaluations of AI-enabled services and platform interventions, often depart from independent and identically distributed (i.i.d.) sampling because assignments may be adaptive, balanced across covariates, or subject to rollout constraints such as exposure, fairness, and budget limits. This paper studies the efficiency benchmark for estimating causal targets in such sequential experiments. We show that every non-anticipating design induces an…

流动性溢价与投资期限AI 解读

We estimate Kyle's (1985) price-impact coefficient $λ$ directly from daily equity order flow and test its ability to forecast the cross-section of subsequent stock returns. Using CRSP data from 2020 to 2025, we construct firm-month measures of signed order flow and two estimators of $\hatλ_{it}$: a within-month price-impact regression and an Amihud-style ratio. Signed order flow strongly predicts contemporaneous and one-month-ahead returns, while volume volatility predicts…

用库存生命周期价值衡量机会成本AI 解读

Measuring the long-term opportunity cost of interventions remains a critical challenge in e-commerce A/B testing. While strategic levers (such as dynamic pricing, ranking algorithms, and promotional campaigns) trigger shifts in consumer behaviour that persist over months, operational constraints necessitate fast decision-making cycles that are typically limited to weekly experimental windows. Standard metrics like revenue and conversion are inherently short-sighted, biasing…

单变量时间序列的自相关式特征相关性度量AI 解读

We propose a model agnostic methodology to measure lag relevance in machine learning forecasting models applied to univariate time series. Particularly, we are working in the context of time series using the frameworks of Ghost variables and Shapley values, together with additive importance measures, to introduce the auto-relevance and partial auto-relevance functions as the lag importance values. Additionally, we propose a novel method to replace absent features in…

结局子抽样纵向两阶段设计的目标最大似然估计AI 解读

We consider efficient estimation of causal parameters in longitudinal two-stage designs with outcome subsampling, motivated by resampling designs in HIV-related mortality studies. In these studies, many participants become lost to follow-up; resampling designs address this by tracing a subset of lost individuals to ascertain their outcomes. Analyses often use inverse-probability-weighted Kaplan-Meier (wKM) estimators that discard longitudinal covariate information and suffer…

潜在高斯离散多变量时间序列的解析标准误AI 解读

Unlike their continuous-valued counterparts, there are no universally preferred methodologies for modeling discrete-valued time series. This is especially problematic in fields such as psychology and education, where repeated-measures data often take the form of count, dichotomous, and ordered categorical variables. To address the need for flexible methodology for analyzing discrete-valued time series data, a copula-style multivariate model defined through deterministic…

存在未测量处理诱发混杂的近端中介分析AI 解读

Mediation analysis provides a central framework for elucidating causal mechanisms, yet its application is often impeded by treatment-induced confounding, under which the widely used natural mediation effects are generally unidentifiable. Interventional effects have been proposed as an alternative when these confounders are observable; however, identifying and estimating interventional effects remains challenging when confounders are unmeasured. In this paper, we address this…

开放比特币指标：来自全节点的可核验经济研究时间序列AI 解读

Bitcoin research increasingly relies on on-chain indicators to study network activity, monetary issuance, transaction demand, miner incentives, coin-age behavior, and long-run monetary dynamics. However, many commonly used Bitcoin metrics are dispersed across commercial platforms, subject to heterogeneous definitions, or not fully reproducible from primary blockchain data. This manuscript introduces Open Bitcoin Metrics (OBM), a reproducible, full-node-derived dataset and…

异方差何时影响推断：针对线性对比的稳健推断理论AI 解读

Conventional heteroskedasticity diagnostics ask whether the conditional variance of the regression disturbance varies with covariates. This paper asks a different question: when does that variation matter for inference on the estimand of interest? The paper develops a contrast-specific theory characterizing when covariance perturbations are inferentially relevant. We show that, for any linear contrast $a'β$ in a linear regression, the difference between the…

结局适配的自动去偏机器学习AI 解读

Parameters of interest in causal inference, such as treatment or policy effects, can often be expressed as linear functionals of an outcome regression function. Automatic debiased machine learning (AutoDML) is a unified framework for obtaining asymptotically normal estimators of such parameters, which requires estimation of both a regression function and a Riesz representer. Existing AutoDML neural network architectures, such as RieszNet and MADNet, use a shared intermediate…

CaSPECT：用有向谱聚类发现因果同质子群AI 解读

We propose \textbf{CaSPECT}, a causal spectral clustering framework for discovering causally homogeneous subgroups from observational data. Rather than clustering in covariate space, CaSPECT defines similarity through the topology of a learned directed acyclic graph (DAG); a bootstrap-stabilised PC algorithm recovers the causal skeleton; a novel \emph{Orientation Validation Score} (OVS) combines PC bootstrap evidence with DirectLiNGAM to orient edges robustly; directed edges…

检索与推理：建筑节能改造推荐的成本受控模型基准AI 解读

Recommending the correct set of energy conservation measures (ECMs) for a building is a structured, multi-label prediction problem in which a task-specific supervised model has weak training signal and a general language model has no grounding in the local building stock. We study this problem on 10,422 real New York City Local Law 87 (LL87) energy-audit records, taking as ground truth the set of ECM categories that certified auditors actually recommended. We make four…

GLARMA 模型的贝叶斯估计及应用AI 解读

This work presents a Bayesian approach for parameter estimation in the class of Generalized Linear Autoregressive Moving Average (GLARMA) models, extending the methodology beyond the common exponential family setting. The proposed framework accommodates positive, double-bounded, and count time series through a unified MCMC-based estimation procedure implemented in \texttt{nimble}. We discuss prior specifications for the model parameters and conduct an extensive Monte Carlo…

对话的因果效应是什么？AI 中介对话的目标量与推断AI 解读

Political scientists increasingly use conversations as treatments. Sometimes these conversations are conducted by humans, but often and increasingly they will be conducted by generative artificial intelligence (AI). AI makes it possible to scale treatments that are responsive, and rich in real-world relevance. But this same interactivity creates specific challenges for causal inference. A respondent may be randomly assigned to a conversational condition, but the conversation…

普吉特海湾地区在线购物行为的伪面板双重差分分析AI 解读

Online shopping is a growing trend, particularly following the COVID-19 lock-downs enacted by many cities. Understanding these trends requires robust panel dat methods. However, panel data (i.e., with repeated measurements of the same observational units) are often unavailable. In this study, we use a propensity score weighting (PSW) approach to adjust repeated cross-sectional travel diary surveys collected in the Puget Sound Regional Council (PSRC) region. The most common…

远程办公：数字产品创新的推动力还是阻碍？AI 解读

As firms adopt divergent policies regarding work-from-home (WFH), the implications of remote work for collaborative and interdependent outcomes such as digital product innovation remain uncertain. This study examines how remote work adoption affects continuous digital product innovation using a panel dataset of mobile applications. We identify firm-level remote work adoption from job postings data and estimate its effects on app innovation using a staggered…

具有设计型方差的交叉拟合调查加权 TMLEAI 解读

Cross-fitting is not a refinement of survey-weighted causal machine learning but, once the nuisances are flexible, what restores valid inference. We study the population average treatment effect under a stratified multistage design, estimated by a survey-aware targeted maximum likelihood estimator (TMLE) whose variance is obtained by Taylor-series linearization of the influence function, treating the primary sampling unit as the replication unit. Our central result is that…

移动平均与自回归过程的似然几何AI 解读

We study the problem of maximum likelihood estimation for moving average (MA) time series models from the perspective of algebraic statistics, with a focus on the structure and number of solutions of their likelihood equations. Of particular interest is to classify the critical points that lead to non-invertible models. We consider the composite likelihood as an alternative estimation method and analyze its critical points. We extend our algebraic analysis to autoregressive…