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基于泊松稀释结构方程模型的计数数据因果 DAG 识别AI 解读

Count-valued variables arise in many scientific and applied settings, yet explicit structural models that allow full identification of causal DAGs from observational data remain limited. The Poisson branching structural causal model (PB-SCM) provides a count-valued analogue of linear structural equation models using binomial thinning and independent Poisson exogenous variables, but its causal DAG is generally only partially identifiable. Building on this framework, we…

部分识别处理效应的广义信仰推断框架AI 解读

Over the past two decades, there has been renewed interest in fiducial inference, a statistical framework originally proposed by R. A. Fisher in the 1930s. Existing contributions, however, have largely focused on point-identified models, where the parameter vector is uniquely determined by the joint distribution of the observables and the model assumptions. This paper develops a unified fiducial inference procedure for partially identified models, thereby extending the…

短面板中随机系数矩的识别与估计AI 解读

We study identification and estimation of moments of random coefficients in short linear panels, allowing the number of heterogeneous coefficients to exceed the number of equations observed for each unit. Under moment homogeneity, different regressor histories impose restrictions on the same moment vector. We give necessary and sufficient conditions for these restrictions to identify moments of a given order, stated in terms of the row spaces generated by the regressor…

最大得分模型与线性规划下的二元分类AI 解读

This paper presents a computationally efficient method for binary classification using Manski's (1975, 1985) maximum score model when covariates are discretely distributed and parameters are partially but not point identified. We establish minimax-regret-optimal classification rules that take account of partial identification of the model's parameters. We bound misclassification probabilities and expected excess regret induced by sampling uncertainty. We also describe an…

利用 AI 生成测量识别缺失数据：弱影子变量方法AI 解读

Across business and social science applications, outcomes are often missing in ways that depend on the unobserved outcomes themselves. In service systems, for example, whether a customer submits a rating depends on the rating they would have provided. Such missing-not-at-random (MNAR) mechanisms make population quantities difficult to identify without strong assumptions on the observation process. Meanwhile, rich unstructured data, such as customer interaction histories, are…

部分识别离散响应模型的非参数贝叶斯推断AI 解读

This paper proposes a nonparametric Bayesian inference framework for partially identified discrete response models. The key observation is that these models map a reduced-form conditional choice probability to an identified set. Consequently, nonparametric Bayesian inference for the conditional probability mass function leads to Bayesian inference for the identified set. The inference framework nests conditional moment inequalities and linear systems with unknown…

正值性违反下修正处理政策的因果效应：部分识别方法AI 解读

Modified treatment policies (MTPs) are interventions based on each individual's natural treatment value. We study mean outcomes under MTPs for continuous treatments, including exposure mixtures. Positivity is the standard sufficient condition for identifying these mean outcomes without extrapolation: policy-generated values remain supported given covariates. With multivariate treatments or continuous covariates, treatment--covariate combinations can be sparse or unsupported.…

部分识别因果效应的模型无关协变量辅助推断AI 解读

Many causal estimands are only partially identifiable since they depend on the unobservable joint distribution between potential outcomes. Stratification on pretreatment covariates can yield sharper bounds; however, unless the covariates are discrete with relatively small support, this approach typically requires binning covariates or estimating the conditional distributions of potential outcomes given the covariates. Binning can result in substantial efficiency loss and…

分类处理下个体化处理规则的部分识别学习AI 解读

We develop a partial identification learning framework for individualized treatment rules (ITRs) with categorical treatments, outcomes, and instrumental variables. Rather than relying on strong causal assumptions required for point identification, our framework leverages causal bounds to characterize the optimal treatment decision. Existing methods for ITR optimization under partial identification are largely restricted to binary treatment settings and the bounds derived by…

权重不确定性下的合成控制：稳健识别与统计推断AI 解读

The synthetic control method estimates causal effects by comparing a treated unit with weighted controls matched on its pre-treatment trajectory. However, validity can be compromised when highly correlated controls leave the synthetic weights weakly determined or when treated-control relationships shift after treatment. We propose a new estimand, the weight-robust treatment effect, defined as the optimizer of a worst-case optimization problem over an uncertainty class of…

未知干扰下利用面板数据学习处理效应AI 解读

When comparison units may also respond to treatment, panel comparisons reflect both the treatment effect and spillovers. If the interference pattern is unknown, observed outcomes alone do not separate the two. I characterize what can nevertheless be learned from panel outcomes under general restrictions, without requiring an exposure mapping or prior classification of affected donors. The framework scales validity bounds for every convex donor weight by its fit before…

部分识别下处理选择的局部渐近理论AI 解读

We provide a new asymptotic framework to derive approximately optimal treatment assignments when sampling noise from data is compounded by fundamental uncertainty due to partial identification. We recenter the reduced-form parameter around its \emph{least-favorable} configuration and consider drifting parameter sequences that yield both diminishing levels of sampling uncertainty and of partial identification. We characterize the limiting decision problem as a normal location…

诚实置信区间的局部渐近功效AI 解读

Confidence intervals that are conservative against an untestable bias, called bias-aware or honest, are now standard in DiD, IV, RD, and factor-model settings. This paper characterises the local power of the tests they induce. Power is governed by the rate of the bias bound relative to the sampling rate, giving three regimes: when the bound vanishes faster than the standard error, conservatism is asymptotically free; when the two are of the same order it costs a bounded,…

利用代理变量对因果效应进行部分识别AI 解读

Proximal causal inference is a framework for evaluating the causal effects in the presence of unmeasured confounding. For point identification, it leverages a pair of proxy variables to identify a bridge function that matches the dependence of potential outcomes or treatment variables on the hidden factors to corresponding functions of observed proxies. Unique identification requires that proxies are sufficiently relevant for hidden factors, a requirement that has previously…

不完整数据下的不平等界限AI 解读

We study inequality measures when outcomes are observed only in intervals, as in historical tabulations, privacy-protected grouped data, and modern surveys. We develop a nonparametric framework for sharp identification and inference with grouped and interval-valued data, covering brackets and overlapping intervals. For a class of inequality indices, sharp bounds are attained by discrete distributions with finite support, reducing the problem to optimization;…

类别型工具变量模型：刻画、部分识别与统计推断AI 解读

We study categorical instrumental variable (IV) models with instrument, treatment and outcome taking finitely many values. We derive a simple closed-form characterization of the set of joint distributions of potential outcomes that are compatible with a given observed data distribution in terms of a minimal set of inequalities. These inequalities unify several different IV models defined by versions of the independence and exclusion restriction assumptions. They lead to…

对提前反应与平行趋势违背的联合敏感性分析AI 解读

Two key identifying assumptions used to justify difference-in-differences are parallel trends and no anticipation, yet both may fail in practice. I propose a class of assumptions that constitute deviations from no anticipation and derive closed-form, sharp bounds for several common treatment effect parameters while simultaneously relaxing parallel trends. Deviations from both assumptions are jointly disciplined using observed pre-trends. When some anticipation is imposed,…

随机对象的单调处理反应与部分识别AI 解读

Monotone treatment response (MTR), monotone treatment selection (MTS), and monotone instrumental variable (MIV) assumptions are widely used to partially identify counterfactual mean outcomes, but existing analyses have focused almost exclusively on scalar outcomes. We develop a unified framework for partial identification with outcomes that take values in a general metric space under these monotonicity restrictions by embedding the metric space into an $L^2$ space and…

大规模贝叶斯 VAR 的条件投影方法AI 解读

We develop fast methods for conditional forecasting and structural scenario analysis with high-dimensional Bayesian vector autoregressions (VARs). Our general framework features a factor structure on the reduced-form errors, which enables fast and order-invariant equation-by-equation estimation; suitably identified factors admit a structural interpretation. The scenarios are defined through separate distributional restrictions on observables, structural shocks and…

非可分面板数据模型中结构与因果效应的线性估计AI 解读

This paper develops linear estimators for structural and causal parameters of nonseparable models using panel data. These models incorporate unobserved, time-varying, individual heterogeneity, which may be correlated with the regressors. Estimation is based on an approximation of a conditional average potential outcome by a linear sieve specification with individual-specific parameters. Effects of interest are estimated by a bias corrected average of individual ridge…

实时宏观经济预测中的数据修订风险AI 解读

Macroeconomic forecasts refer to outcomes that are first released and then revised. A 90 percent interval for the first GDP release, a six-month value, or a latest-value benchmark is not the same uncertainty statement. We ask how revision risk evolves through the release cycle and what can be reported in real time when later-outcome errors are scarce. We decompose later-outcome MSE into preliminary forecast risk, revision risk, and their covariance. In SPF data,…

辅助矩约束下的部分识别AI 解读

Partial identification is often set aside in practice because the identification regions it delivers are too wide to be useful, pushing researchers toward strong assumptions that buy point identification at the cost of credibility. We show that a source of information already sitting in most interval-valued datasets can fix this without adding any assumption at all. When an outcome is reported only as an interval---because a data custodian bracketed, top-coded, or formally…

潜在解释变量具有多个非线性测量时的部分识别AI 解读

We study linear regression when the regressor is latent and observed only through multiple noisy measurements, each a smooth but possibly nonlinear function of the latent variable. The problem is acute in the measurement of occupational exposure to artificial intelligence, where competing scores yield downstream estimates that differ by a factor of eleven. A regression on any single measurement recovers a source-specific coefficient rather than the structural one. We fix the…

高维 U 统计量中显式依赖阶数的线性化AI 解读

We give an order-explicit large deviation bound for the difference between a high-dimensional $U$-statistic and its Hájek projection. In particular, we show that any $U$-statistic of order $b$ on $n$ observations, with a $d$-dimensional kernel whose coordinates have $ψ_1$-Orlicz norm at most $φ$, has a maximum deviation from its Hájek projection of order $O_p(φb n^{-1}\log^2(dn))$. The proof relies on the development of novel order-explicit moment inequalities for…