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《移动健康中二元结局的时变因果偏离效应估计》讨论AI 解读

Summary In this discussion, we examine the contributions of Qian et al. (2021) and potential applications of the newly developed estimator for the causal excursion effect in binary outcome data. Specifically, we consider extension of their method to count outcomes and observational data, propose an alternative use of their method for analysing excursion effect trajectories and discuss ways of improving estimator efficiency.

通过降维构建简约的个性化剂量探索模型AI 解读

Summary Learning an individualized dose rule in personalized medicine is a challenging statistical problem. Existing methods often suffer from the curse of dimensionality, especially when the decision function is estimated nonparametrically. To tackle this problem, we propose a dimension reduction framework that effectively reduces the estimation to an optimization on a lower-dimensional subspace of the covariates. We exploit the fact that the individualized dose rule can be…

存在中间混杂变量时的非参数有效因果中介分析AI 解读

Summary Interventional effects for mediation analysis were proposed as a solution to the lack of identifiability of natural (in)direct effects in the presence of a mediator-outcome confounder affected by exposure. We present a theoretical and computational study of the properties of the interventional (in)direct effect estimands based on the efficient influence function in the nonparametric statistical model. We use the efficient influence function to develop two…

移动健康中二元结局的时变因果偏离效应估计AI 解读

Summary Advances in digital technology and wearables have made it possible to deliver behavioural mobile health interventions to individuals in their everyday lives. Micro-randomized trials are increasingly used to provide data to inform the construction of these interventions. In a micro-randomized trial, each individual is repeatedly randomized among multiple intervention options, often hundreds or even thousands of times over the course of the trial. The work reported in…

双重差分特征价格法AI 解读

Cross-sectional estimates of hedonic price functions theoretically recover marginal values for characteristics but face endogeneity problems. Consequently, economists have introduced difference-in-differences and other quasi-experimental econometric methods into hedonic models. Unfortunately, the welfare interpretation of these estimands has not been clear. This paper shows that quasi-experimental hedonics can identify the movement along the ex post price function. It…

两样本汇总数据孟德尔随机化中的去偏逆方差加权估计AI 解读

Mendelian randomization (MR) has become a popular approach to study the effect of a modifiable exposure on an outcome by using genetic variants as instrumental variables. A challenge in MR is that each genetic variant explains a relatively small proportion of variance in the exposure and there are many such variants, a setting known as many weak instruments. To this end, we provide a theoretical characterization of the statistical properties of two popular estimators in MR:…

非法药物使用、教育与劳动力市场结果AI 解读

In this paper we investigate the causal effects of consuming illegal drugs on educational attainment, employment, and wages. To identify these effects we develop and estimate a dynamic structural model to jointly consider decisions of whether to consume drugs, attend school, participate in the labor force, and save.

利用二元工具变量部分识别非可分模型AI 解读

In this study, we explore the partial identification of nonseparable models with continuous endogenous and binary instrumental variables. We show that the structural function is partially identified when it is monotone or concave in the explanatory variable. D’Haultfœuille and Février (2015, Econometrica 83(3), 1199–1210) and Torgovitsky (2015, Econometrica 83(3), 1185–1197) prove the point identification of the structural function under a key assumption that the conditional…

考虑网络变化的处理效应AI 解读

Abstract Networks may rewire in response to interventions. We propose a measure of the treatment effect when an intervention affects the structure of a social network. We develop a treatment-response model that incorporates dynamic peer effects and provide its identification conditions and the associated instrumental-variable strategy. We illustrate our estimation procedure using a panel data set containing information on a financial network before and after a field…

利用因果推断与机器学习评估1990年《清洁空气法》修正案的健康影响AI 解读

We develop a causal inference approach to estimate the number of adverse health events that were prevented due to changes in exposure to multiple pollutants attributable to a large-scale air quality intervention/regulation, with a focus on the 1990 Clean Air Act Amendments (CAAA). We introduce a causal estimand called the Total Events Avoided (TEA) by the regulation, defined as the difference in the number of health events expected under the no-regulation pollution exposures…

临床试验中经选择子群的统计推断AI 解读

When existing clinical trial data suggest a promising subgroup, we must address the question of how good the selected subgroup really is. The usual statistical inference applied to the selected subgroup, assuming that the subgroup is chosen independent of the data, may lead to an overly optimistic evaluation of the selected subgroup. In this article, we address the issue of selection bias and develop a de-biasing bootstrap inference procedure for the best selected subgroup…

依从者随机直接效应：识别与稳健估计AI 解读

Mediation analysis is critical to understanding the mechanisms underlying exposure-outcome relationships. In this paper, we identify the instrumental variable-direct effect of the exposure on the outcome not through the mediator, using randomization of the instrument. We call this estimand the complier stochastic direct effect (CSDE). To our knowledge, such an estimand has not previously been considered or estimated. We propose and evaluate several estimators for the CSDE: a…

运行变量存在组别特定测量误差时的断点回归校正AI 解读

When the running variable in a regression discontinuity (RD) design is measured with error, identification of the local average treatment effect of interest will typically fail. While the form of this measurement error varies across applications, in many cases the measurement error structure is heterogeneous across different groups of observations. We develop a novel measurement error correction procedure capable of addressing heterogeneous mismeasurement structures by…

持续时间结果下处理效应异质性的非参数检验AI 解读

This article proposes different tests for treatment effect heterogeneity when the outcome of interest, typically a duration variable, may be right-censored. The proposed tests study whether a policy (1) has zero distributional (average) effect for all subpopulations defined by covariate values, and (2) has homogeneous average effect across different subpopulations. The proposed tests are based on two-step Kaplan–Meier integrals and do not rely on parametric distributional…

谁是关键参与者？青少年违法行为的网络分析AI 解读

This article presents a methodology for empirically identifying the key player, whose removal from the network leads to the optimal change in aggregate activity level in equilibrium [Ballester, C., Calvó-Armengol, A., and Zenou, Y. (2006), “Who’s Who in Networks. Wanted: The Key Player,” Econometrica, 74: 1403–1417], allowing the network links to rewire after the removal of the key player. First, we propose an IV-based estimation strategy for the social-interaction effect,…

投资激增之后：投资事件与企业绩效AI 解读

Our study aims at investigating the relationship between investment spikes and subsequent productivity development at the firm level. We propose a novel identification scheme for the effects of an investment spike, using matching techniques and a tailored econometric modeling. It allows us to find efficiency differentials against matched firms in periods adjacent to the spike. We showed that TFP persistently falls after an investment spike, which is consistent with…

提前规划能否改善社区？来自坦桑尼亚的长期证据AI 解读

Africa’s demand for urban housing is soaring, even as it faces a proliferation of slums. In this setting, can modest infrastructure investments in greenfield areas where people subsequently build their own houses facilitate long-run neighborhood development? We study Sites and Services projects implemented in seven Tanzanian cities during the 1970s and 1980s, and we use a spatial regression discontinuity design to compare greenfield areas that were treated (de novo) with…

内生二元处理下适用于任意响应类型的工具残差估计AI 解读

Abstract Given an endogenous/confounded binary treatment D, a response Y with its potential versions (Y0, Y1) and covariates X, finding the treatment effect is difficult if Y is not continuous, even when a binary instrumental variable (IV) Z is available. We show that, for any form of Y (continuous, binary, mixed,…), there exists a decomposition Y = μ0(X) + μ1(X)D + error with E(error|Z,X) = 0, where μ1(X)≡E(Y1-Y0|complier,X) and ‘compliers’ are those who get treated if and…

二元工具变量与删失数据下的因果分位数效应估计AI 解读

Abstract The causal effect of a treatment is of fundamental interest in the social, biological and health sciences. Instrumental variable (IV) methods are commonly used to determine causal treatment effects in the presence of unmeasured confounding. In this work, we study a new binary IV framework with randomly censored outcomes where we propose to quantify the causal treatment effect by the concept of complier quantile causal effect (CQCE). The CQCE is identifiable under…

提高观察性研究中异常结局检验功效的自适应方法AI 解读

Abstract In many observational studies, the interest is in the effect of treatment on bad, aberrant outcomes rather than the average outcome. For such settings, the traditional approach is to define a dichotomous outcome indicating aberration from a continuous score and use the Mantel–Haenszel test with matched data. For example, studies of determinants of poor child growth use the World Health Organization’s definition of child stunting being height-for-age z-score ≤ − 2.…

类别解释变量的收缩估计AI 解读

This paper introduces a flexible regularization approach that reduces point estimation risk of group means stemming from e.g. categorical regressors, (quasi-)experimental data or panel data models. The loss function is penalized by adding weighted squared ℓ2-norm differences between group location parameters and informative first stage estimates. Under quadratic loss, the penalized estimation problem has a simple interpretable closed-form solution that nests methods…

以实验者满意化决策模型指导受损实验的有限样本推断AI 解读

Summary This paper presents a simple decision-theoretic economic approach for analysing social experiments with compromised random assignment protocols that are only partially documented. We model administratively constrained experimenters who satisfice in seeking covariate balance. We develop design-based small-sample hypothesis tests that use worst-case (least favourable) randomization null distributions. Our approach accommodates a variety of compromised experiments,…

非可分面板模型的低秩近似AI 解读

Summary We provide estimation methods for nonseparable panel models based on low-rank factor structure approximations. The factor structures are estimated by matrix-completion methods to deal with the computational challenges of principal component analysis in the presence of missing data. We show that the resulting estimators are consistent in large panels, but suffer from approximation and shrinkage biases. We correct these biases using matching and…

条件平均处理效应及其他因果函数的去偏机器学习AI 解读

Summary This paper provides estimation and inference methods for the best linear predictor (approximation) of a structural function, such as conditional average structural and treatment effects, and structural derivatives, based on modern machine learning tools. We represent this structural function as a conditional expectation of an unbiased signal that depends on a nuisance parameter, which we estimate by modern machine learning techniques. We first adjust the signal to…