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结局与协变量缺失时随机实验中的协变量调整AI 解读

Summary Covariate adjustment can improve precision in analysing randomized experiments. With fully observed data, regression adjustment and propensity score weighting are asymptotically equivalent in improving efficiency over unadjusted analysis. When some outcomes are missing, we consider combining these two adjustment methods with the inverse probability of observation weighting for handling missing outcomes, and show that the equivalence between the two methods breaks…

算法辅助人类决策的最优策略AI 解读

Summary We consider optimal regimes for algorithm-assisted human decision-making. Such regimes are decision functions of measured pre-treatment variables and, by leveraging natural treatment values, enjoy a superoptimality property whereby they are guaranteed to outperform conventional optimal regimes. When there is unmeasured confounding, the benefit of using superoptimal regimes can be considerable. When there is no unmeasured confounding, superoptimal regimes are…

用于社区检测的网络调整协变量AI 解读

Summary Community detection is a crucial task in network analysis that can be significantly improved by incorporating subject-level information, ie, covariates. Existing methods have shown the effectiveness of using covariates on the low-degree nodes, but rarely discuss the case where communities have significantly different density levels, ie, multiscale networks. In this paper, we introduce a novel method that addresses this challenge by constructing network-adjusted…

时间一致中心极限定理与渐近置信序列AI 解读

Confidence intervals based on the central limit theorem (CLT) are a cornerstone of classical statistics. Despite being only asymptotically valid, they are ubiquitous because they permit statistical inference under weak assumptions and can often be applied to problems even when nonasymptotic inference is impossible. This paper introduces time-uniform analogues of such asymptotic confidence intervals, adding to the literature on confidence sequences (CS)—sequences of…

通过树计数高效检验网络相关性AI 解读

We propose a new procedure for testing whether two networks are edge-correlated through some latent vertex correspondence. The test statistic is based on counting the cooccurrences of signed trees for a family of nonisomorphic trees. When the two networks are Erdős–Rényi random graphs G(n,q) that are either independent or correlated with correlation coefficient ρ, our test runs in n2+o(1) time and succeeds with high probability as n→∞, provided that nmin{q,1−q}≥n−o(1) and…

个人税收是否影响投资决策与股票收益？AI 解读

This paper studies the causal effects of personal investment taxes on stock returns and the financial decisions of companies. I exploit a change in legislation in 2013 which allowed stocks listed on the Alternative Investment Market, a sub-market of the London Stock Exchange, to be held in capital gains and dividend tax-exempt investment accounts for the first time. Using a difference-in-differences approach, I find that excess stock returns decreased by their…

含交互固定效应的核范数正则化分位数回归AI 解读

This paper studies large N and large T conditional quantile panel data models with interactive fixed effects. We propose a nuclear norm penalized estimator of the coefficients on the covariates and the low-rank matrix formed by the interactive fixed effects. The estimator solves a convex minimization problem, not requiring pre-estimation of the (number of) interactive fixed effects. It also allows the number of covariates to grow slowly with N and T . We derive an error…

含协变量的分布泛函序贯处理分配AI 解读

We consider a sequential treatment problem with covariates. Given a realization of the covariate vector, instead of targeting the treatment with highest conditional expectation, the decision-maker targets the treatment which maximizes a general functional of the conditional potential outcome distribution, e.g., a conditional quantile, trimmed mean, or a socioeconomic functional such as an inequality, welfare, or poverty measure. We develop expected regret lower bounds for…

未观测混杂下处理效应的模型辅助敏感性分析：正则化校准估计AI 解读

Abstract Consider sensitivity analysis for estimating average treatment effects under unmeasured confounding, assumed to satisfy a marginal sensitivity model. At the population level, we provide new representations for the sharp population bounds and doubly robust estimating functions. We also derive new, relaxed population bounds, depending on weighted linear outcome quantile regression. At the sample level, we develop new methods and theory for obtaining not only doubly…

匹配设计中的协变量自适应随机化推断AI 解读

Abstract It is common to conduct causal inference in matched observational studies by proceeding as though treatment assignments within matched sets are assigned uniformly at random and using this distribution as the basis for inference. This approach ignores observed discrepancies in matched sets that may be consequential for the distribution of treatment, which are succinctly captured by within-set differences in the propensity score. We address this problem via…

大量混合工具变量与控制变量下的内生处理效应估计AI 解读

Abstract Instrumental variables (IVs) and control variables are frequently used to assist researchers in investigating endogenous treatment effects. When used together, their identities are typically assumed to be known. However, in many practical situations, one is faced with a large and mixed set of covariates, some of which can serve as excluded IVs, some can serve as control variables, whereas others should be discarded from the model. It is often not possible to…

研发补助的因果效应：来自断点回归的证据AI 解读

Abstract We leverage the discontinuity in the assignment mechanism of the Small and Medium Enterprise Instrument—the first European research and development (R&D) subsidy targeting small firms—to provide the broadest quasi-experimental evidence on R&D grants over both geographical and sectoral scopes. Grants trigger sizable impacts on a wide range of firm-level outcomes. Heterogeneous effects are consistent with grants reducing financial frictions. This reduction is due to…

从局部平均处理效应到平均处理效应：贝叶斯方法AI 解读

We develop a Bayesian model that produces a posterior distribution of the marginal treatment effect (MTE) function. The method provides researchers with a principled way to extrapolate from the observed moments using flexible assumptions, thereby allowing researchers to generate plausible ranges of important and potentially policy-relevant quantities of interest. We then use the model to propose a natural decomposition of the posterior variance into “statistical…

大面板中的近似斜率同质性检验AI 解读

In this paper, we introduce new inference methods for slope homogeneity in large regression panels. While most existing tests are developed for the hypothesis of slope homogeneity (equality of all individual slopes), we propose to test the more realistic relaxation of approximate slope homogeneity (similarity of all slopes). We present new test statistics for dense and sparse alternatives to approximate homogeneity. In the dense setting, the main focus of this paper, we…

具有低秩系数矩阵的多元时空模型AI 解读

Multivariate spatiotemporal data arise frequently in practical applications, often involving complex dependencies across cross-sectional units, time points and multivariate variables. In the literature, few studies jointly model the dependence in three dimensions. To simultaneously model the cross-sectional, dynamic and cross-variable dependence, we propose a multivariate reduced-rank spatiotemporal model. By imposing the low-rank assumption on the spatial influence matrix,…

短时间维度面板中的异质自回归AI 解读

Summary This paper considers a first‐order autoregressive (AR) panel data model with individual‐specific effects and heterogeneous AR coefficients defined on the interval , thus allowing for some of the individual processes to have unit roots. It proposes estimators for the moments of the cross‐sectional distribution of the AR coefficients, assuming a random coefficient model for the AR coefficients without imposing any restrictions on the fixed effects. It is shown that the…

面板处理效应测量：因子模型还是线性投影？AI 解读

Summary We discuss methods of measuring the treatment effects of a unit through the use of other units in panel data by either the factor‐based (FB) approach or the linear projection (LP) approach under different sample configurations of cross‐sectional dimension and time series dimension . We show that the LP approach in general yields smaller mean square prediction error than the FB approach when either both and are large or fixed and or fixed and large. The Monte Carlo…

用凝聚型层次聚类选择有效工具变量AI 解读

Summary We propose a procedure that combines hierarchical clustering with a test of overidentifying restrictions for selecting valid instrumental variables (IV) from a large set of IVs. Some of these IVs may be invalid in that they fail the exclusion restriction. We show that if the largest group of IVs is valid, our method achieves oracle properties. Unlike existing techniques, our work deals with multiple endogenous regressors. Simulation results suggest an advantageous…

固定效应动态离散选择模型中平均边际效应的识别AI 解读

Abstract In nonlinear panel data models, fixed-effects methods are often criticized because they cannot identify average marginal effects (AMEs) in short panels. In contrast with that criticism, we prove the point identification of different AMEs, including causal effects of changes in the lagged dependent variable or the last choice's duration, in a panel dynamic logit model for T as small as three. Our proofs are constructive and provide simple closed-form expressions for…

纵向与面板数据的因果模型：综述AI 解读

Summary In this survey we discuss the recent causal panel data literature. This recent literature has focused on credibly estimating causal effects of binary interventions in settings with longitudinal data, emphasising practical advice for empirical researchers. It pays particular attention to heterogeneity in the causal effects, often in situations where few units are treated and with particular structures on the assignment pattern. The literature has extended earlier work…

用于模型检验的高斯过程方法AI 解读

This paper proposes a Gaussian process (GP) approach for testing conditional moment restrictions. Tests are based on squared Neyman orthogonal function-parametric processes integrated with respect to a GP distribution. This methodology leads to a general unified framework of kernel-based tests having the following properties: (i) bootstrap tests are easy to implement in the presence of nuisance parameters (they are simple quadratic forms, and there is no need to reestimate…

多层随机区块模型的计算与统计阈值AI 解读

We study the problem of community recovery and detection in multi-layer stochastic block models, focusing on the critical network density threshold for consistent community structure inference. Using a prototypical two-block model, we reveal a computational barrier for such multilayer stochastic block models that does not exist for its single-layer counterpart: When there are no computational constraints, the density threshold depends linearly on the number of layers.…

利用低阶多项式研究图函数估计的计算下界AI 解读

Graphon estimation has been one of the most fundamental problems in network analysis and has received considerable attention in the past decade. From the statistical perspective, the minimax error rate of graphon estimation has been established by (Ann. Statist. 43 (2015) 2624–2652) for both stochastic block model (SBM) and nonparametric graphon estimation. The statistical optimal estimators are based on constrained least squares and have computational complexity exponential…

环境不变线性最小二乘AI 解读

This paper considers a multi-environment linear regression model in which data from multiple experimental settings are collected. The joint distribution of the response variable and covariates may vary across different environments, yet the conditional expectations of the response variable, given the unknown set of important variables, are invariant. Such a statistical model is related to the problem of endogeneity, causal inference, and transfer learning. The motivation…