每日简报

函数型因果推断框架中的广义倾向得分加权AI 解读

Estimating causal effects in observational studies requires adjustment for confounding, a task that becomes challenging when the exposure is a function observed over a continuous domain rather than a scalar variable. We develop a functional propensity score weighting framework that achieves covariate balance by removing dependence between time-varying treatments and observed confounders, thereby enabling estimation of marginal causal effects in settings with functional…

干扰成分投影后的识别与信息AI 解读

Empirical work often removes fixed effects, latent factors, or high-dimensional controls before estimating structural relationships. These transformations reduce confounding but may also remove identifying variation. We study linear panel IV after one equation-compatible nuisance projection under two-way dependence. The projected Jacobian determines which structural directions remain visible; the projected-score law determines their precision; and, on Gaussian fixed-rank…

存在未观测效应修饰变量时冲突试验的回归式近端协调AI 解读

Randomized controlled trials with similar protocols may yield conflicting findings when the distribution of relevant effect modifiers differs across study populations. Yet no formal statistical framework exists for defining and assessing whether conflicting trials are reconcilable, despite the importance of this question for evidence synthesis and regulatory decision making. To address this gap, we develop a causal inference framework for evaluating conditional and marginal…

利用因果森林估计出行方式转变的异质性AI 解读

Objectives: While causal analysis of travel behavior is an emerging field, estimating heterogeneity in mode choice through causal modeling remains unexplored. This study demonstrates the application of a novel causal method, causal forest, to quantify the heterogeneity in travel mode choice shifts caused by the COVID-19 pandemic. Methods: We applied causal forests, a non-parametric causal machine learning method, to 802,935 trip records from the 2017 and 2022 waves of the…

对哪个目标量最好？时变处理的纵向匹配与目标试验模拟已知真值基准AI 解读

On a non-collapsible survival mechanism, longitudinal-matching and target-trial-emulation methods are not competing estimators of one truth but answers to different causal questions, so a benchmark that scores them against a single "true hazard ratio" fabricates bias. We provide the direct comparison of relative efficiency, variance estimation, and model sensitivity that reviews find lacking. On a deliberately non-collapsible continuous-time Cox mechanism with known truth,…

网络实验的设计型极小极大理论AI 解读

Network experiments are used throughout the social and medical sciences to investigate causal effects under the presence of interference. While a large body of work has developed improved statistical procedures, the fundamental limits of statistical estimation in these settings is less well understood. In this paper, we develop and investigate a design-based theory of minimax risk for network experiments under an arbitrary neighborhood interference model. Our notion of…

获益—风险评价中结局期望排序概率的统一因果推断框架AI 解读

We developed a unified covariate-adjusted causal inference framework for estimating the desirability of outcome ranking (DOOR) probability for benefit-risk evaluation in randomized trials and observational studies. The framework expresses the DOOR probability as a bilinear functional of the marginal ordinal outcome distributions under the two treatment strategies, estimates conditional ordinal distributions through sequential risk-set hazards, and derives the efficient…

网络模型中基于成对差分的分布回归方法AI 解读

I develop an estimation and inference framework for distribution regression in dyadic network settings with two-way fixed effects that vary across thresholds of the outcome. I show that identification of the structural parameters is achieved through binarization of the outcome at each threshold, and estimate the model by conditional maximum likelihood, which "differences out" the fixed effects and circumvents the incidental parameter problem. The estimator remains…

利用负对照结局事件时间提高传染病预防试验效率AI 解读

Baseline covariate adjustment can enhance the efficiency of randomized trials by improving precision of treatment effect estimates. However, the precision gain depends on how strongly the baseline covariates are prognostic for the primary outcome. In randomized trials of infectious disease prevention interventions (e.g., vaccines or passively administered antibodies), an individual's exposure to the pathogen is a leading prognostic factor but is rarely measurable at…

对抗式因果干预证伪AI 解读

Generative models can reproduce an observational distribution while encoding an incorrect causal structure. We study a sequential game in which a structural causal generator proposes observational and interventional distributions, while an adversarial experimentalist selects interventions intended to maximally falsify the generator. The discriminator is therefore not merely a real-versus-synthetic classifier: it is indexed by an intervention and tests whether the generator…

目标加权 Neyman 分配：总体迁移下异质性处理效应的实验设计AI 解读

Randomized experiments are often run in one population to guide decisions in another. Allocating by experimental proportions wastes budget on groups that rarely appear in deployment, whereas allocating by deployment proportions under-samples groups that are hard to measure precisely. We propose \textbf{TWNA} (Target-Weighted Neyman Allocation), a two-stage stratified design that uses pilot estimates of group--arm outcome variances to allocate final-stage sample sizes and…

生存结局协变量调整中平衡加权与增广方法的联系AI 解读

Covariate adjustment improves the efficiency of treatment-effect analyses in randomized clinical trials, provided the adjustment targets the correct quantity. For time-to-event endpoints, two marginal targets are of primary interest: the log-rank test for the presence of a treatment effect and the marginal hazard ratio for its magnitude. Existing covariate adjustment approaches reach these targets by different ways. Augmentation adjusts the log-rank score by regressing…

层级复合终点的连续加权胜率比AI 解读

Hierarchical composite endpoints are commonly used in clinical trials when component out- comes differ in clinical importance. The win ratio compares patients across treatment groups according to a pre-specified order of clinical priority and has an intuitive interpretation. A strict hierarchical rule, however, uses a lower-priority endpoint only when all higher-priority endpoints are tied or non-informative. This may reduce power when the treatment effect is mainly ex-…

近端因果框架下的个体化函数型处理规则AI 解读

Estimating individualized treatment regimes (ITRs) is fundamental in data-driven personalized decision-making problems, such as precision medicine. Most of the ITR literature either focuses on categorical/continuous treatments or assumes no unmeasured confounding. In this paper, we make the first attempt to estimate the optimal individualized functional treatment regime (IFTR) for observational data where the treatment is a function and unmeasured confounding is present. We…

通向同一答案的三条路径：协调 AIPW、TMLE 与双重机器学习AI 解读

Augmented inverse-probability weighting (AIPW), targeted maximum likelihood estimation (TMLE), and double/debiased machine learning (DML) are three routes to the same efficient influence function for the average treatment effect --- settled theory we treat as background. This tutorial's contribution is its worked, shared-nuisance reconciliation on real data: what a practitioner must actually match for the routes, and the software packages, to agree. Working the effect of…

交错双重差分中的部分同质性AI 解读

In staggered difference-in-differences (DiD) designs, units enter treatment at different calendar times, so the treatment effect is not a single number but a set of Cohort-Average Treatment effects on the Treated (CATTs), one per cohort-time cell. Estimating every CATT as its own parameter, as the standard fully flexible estimator does, is unbiased but inefficient when some of these effects are in fact equal, whereas pooling them all into a single two-way fixed effects…

因果模型的推断等价类AI 解读

Causal models in the same Markov equivalence class are, in the absence of strong assumptions, statistically indistinguishable at any sample size. For modest sample sizes there is also the possibility that several such classes are compatible with the data, pointing to a confidence set of causal models as the appropriate presentation of evidence. Non-identifiability of Gaussian causal models from the same Markov equivalence class corresponds to a plurality of…

Cox 模型中的亚组处理效应一致性检验AI 解读

An overall treatment effect in a clinical trial may inadequately represent particular patient subgroups, creating uncertainty about whether a population-level efficacy conclusion can legitimately be transferred to them. Conventional interaction tests only investigate whether subgroup-specific treatment effects are exactly equal or not, but cannot detect whether the differences are small enough to be clinically negligible. We develop a formal framework for assessing subgroup…

通过分布映射实现反事实公平的强化学习AI 解读

Reinforcement learning (RL) seeks to optimize sequential decisions to maximize population-level benefits over time. However, when deployed in high-stakes settings such as healthcare, RL decisions might systematically restrict some subpopulation's access to valuable services in a manner contrary to the values and goals of stakeholders. Counterfactual fairness (CF) offers a promising framework to address this problem based on causal reasoning. This paper develops a data…

短面板中的平稳误差与分位数回归AI 解读

This paper studies a linear panel model with an unrestricted individual effect and a time- stationary idiosyncratic disturbance. We first show that stationarity is a strong restriction in a quantile model. In a linear conditional quantile specification with quantile-dependent slopes, equality of the conditional residual distributions across periods generically forces the slope coefficient to be constant over the quantile index. Thus, a stationary-error model identifies a…

半合成数据基准可反驳但不能认证：因果模拟中的投影与正则性AI 解读

Plasmode simulation - resampling covariates, and often treatment, from a real cohort and regenerating the outcome from a model with an injected effect - is a standard tool for comparing causal-inference estimators. We argue that its validity turns on two separable design choices, and that separating them clarifies a recent debate over resampling versus regenerating the treatment. Our central message limits what such a benchmark can show: a plasmode can refute a universal…

同伴效应中的不对称性AI 解读

Individuals are often influenced by their peers because deviating from prevailing behavior entails social costs. However, existing peer effects models typically assume that individuals respond similarly to peers who perform better or worse than they do. This paper introduces a novel structural model of asymmetric peer effects in which conformity incentives depend on whether individuals perform below or above each of their peers. We establish that the model admits a unique…

个体因果效应的局部保形预测AI 解读

Standard CATE estimators become inadequate under strong treatment-effect heterogeneity: confidence intervals for conditional means need not cover individual counterfactual effects. We propose an Individualized Causal Prediction (ICP) framework that constructs finite-sample valid conformal prediction intervals for the individual causal effect of a specific query unit. The method localizes calibration to a causally relevant neighborhood using cosine similarity weighted by…

广义 AKM：工资分解中的灵活控制与交互作用AI 解读

How much wage dispersion is attributed to workers, firms, and their sorting depends on how wages are adjusted for observed characteristics. Standard AKM decompositions impose a known linear adjustment. We develop Generalized AKM, a framework that permits an unknown smooth covariate function and group-specific nonlinear interactions while preserving the original variance components. We prove consistency and asymptotic normality with heteroskedastic errors and many fixed…