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连续暴露与二元结局匹配观察研究的敏感性分析AI 解读

Summary Matching is one of the most widely used study designs for adjusting for measured confounders in observational studies. However, unmeasured confounding may exist and cannot be removed by matching. Therefore, a sensitivity analysis is typically needed to assess a causal conclusion’s sensitivity to unmeasured confounding. Sensitivity analysis frameworks for binary exposures have been well established for various matching designs and are commonly used in various studies.…

序数结局下观察研究与不完全随机试验中获益指标的尖锐符号非参数界AI 解读

Abstract The probability of benefit can be a valuable and meaningful measure of treatment effect. Particularly for an ordinal outcome, it can have an intuitive interpretation. Unfortunately, this measure, and variations of it, are not identifiable even in randomized trials with perfect compliance. There is, for this reason, a long literature on nonparametric bounds for unidentifiable measures of benefit. These have primarily focused on perfect randomized trial settings and…

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…

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…

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…

非线性选择模型中的截距估计AI 解读

We propose various semiparametric estimators for nonlinear selection models, where slope and intercept can be separately identified. When the selection equation satisfies a monotonic index restriction, we suggest a local polynomial estimator, using only observations for which the marginal cumulative distribution function of the instrument index is close to one. Data-driven procedures such as cross-validation may be used to select the bandwidth for this estimator. We then…

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…

Judith ter Schure 对《安全检验》的讨论AI 解读

Once there was a randomized controlled trial (RCT) that studied the recurrence of stroke. It had assumed for the treatment of interest a 40% reduction in hazard, and had set the sample size accordingly (Kamel et al., 2024). The RCT found a 95% confidence interval of (−55%, 36%) and concluded no statistical significance. ‘Absence of evidence is not evidence of absence’ said the statisticians (Altman & Bland, 1995; Harrell, 2024). But the story goes that the sample size was…

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…

Abstract We develop a framework for difference-in-differences designs with staggered treatment adoption and heterogeneous causal effects. We show that conventional regression-based estimators fail to provide unbiased estimates of relevant estimands absent strong restrictions on treatment-effect homogeneity. We then derive the efficient estimator addressing this challenge, which takes an intuitive “imputation” form when treatment-effect heterogeneity is unrestricted. We…

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…

高维时间序列的一致因果推断AI 解读

A methodology for high-dimensional causal inference in a time series context is introduced. Time series dynamics are captured by a Gaussian copula, and estimation of the marginal distribution of the data is not required. The procedure can consistently identify the parameters that describe the dynamics of the process and the conditional causal relations among the possibly high-dimensional variables, under sparsity conditions. Identification of the causal relations is in 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…

皇家经济学会2021年年会萨根讲座AI 解读

Each year, The Econometrics Journal sponsors a plenary lecture at the Annual Conference of the Royal Economic Society. This Sargan Lecture commemorates the fundamental contributions to and profound influence on econometrics by (John) Denis Sargan. It does so by promoting econometric theory and methods with substantive direct or potential value in applications and their actual empirical application. It is chaired by an editor of The Econometrics Journal and published in the…

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…

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…

高维混杂下平均处理效应的去偏逆倾向得分加权AI 解读

We consider estimation of average treatment effects given observational data with high-dimensional pretreatment variables. Existing methods for this problem typically assume some form of sparsity for the regression functions. In this work, we introduce a debiased inverse propensity score weighting (DIPW) scheme for average treatment effect estimation that delivers n-consistent estimates when the propensity score follows a sparse logistic regression model; the outcome…