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分位数回归对一般结构函数识别了什么？AI 解读

This paper shows what quantile regressions identify for general structural functions. Under fairly mild conditions, the quantile partial derivative identifies a weighted average of heterogeneous structural partial effects among the subpopulation of individuals at the conditional quantile of interest. This result justifies the use of quantile regressions as means of measuring heterogeneous causal effects for a general class of structural functions with multiple unobservables.

条件处理效应的非参数检验：单性别学校学业表现的应用AI 解读

We develop a general class of nonparametric tests for treatment effects conditional on covariates. We consider a wide spectrum of null hypotheses regarding conditional treatment effects, including the following: (a) the null hypothesis of the conditional stochastic dominance between treatment and control groups; (b) the null hypothesis that the conditional average treatment effect is nonpositive for each value of covariates; (c) the null hypothesis of no distributional (or…

匹配队列研究中的半参数因果推断AI 解读

Odds ratios can be estimated in case-control studies using standard logistic regression, ignoring the outcome-dependent sampling. In this paper we discuss an analogous result for treatment effects on the treated in matched cohort studies. Specifically, in studies where a sample of treated subjects is observed along with a separate sample of possibly matched controls, we show that efficient and doubly robust estimators of effects on the treated are computationally equivalent…

两水平析因设计的潜在结果因果推断AI 解读

Summary A framework for causal inference from two-level factorial designs is proposed, which uses potential outcomes to define causal effects. The paper explores the effect of non-additivity of unit level treatment effects on Neyman's repeated sampling approach for estimation of causal effects and on Fisher's randomization tests on sharp null hypotheses in these designs. The framework allows for statistical inference from a finite population, permits definition and…

删失数据下非参数遗传风险函数的有效估计AI 解读

With an increasing number of causal genes discovered for complex human disorders, it is crucial to assess the genetic risk of disease onset for individuals who are carriers of these causal mutations and compare the distribution of age-at-onset with that in non-carriers. In many genetic epidemiological studies aiming at estimating causal gene effect on disease, the age-at-onset of disease is subject to censoring. In addition, some individuals' mutation carrier or non-carrier…

结合匹配与非参数工具变量估计：理论及积极劳动力市场政策评估AI 解读

Summary We show how instrumental variable and matching estimators can be combined in order to identify a broader array of treatment effects. Instrumental variable (IV) estimators are known to estimate effects only for the compliers, representing a subset of the entire population. By combining IV with matching, we can estimate the treatment effects for the always‐ and never‐takers as well. Since in many cases these groups are the (endogenous) outcome of some assignment…

Summary This paper provides doubly robust estimators for treatment effect parameters which are defined in a multivalued treatment effect framework. We apply this method to the unique dataset of the 1970 British Cohort Study (BCS70) to estimate returns to various levels of schooling. The analysis is carried out for female and male samples separately to capture possible gender differences. Average returns are estimated for the entire population, as well as conditional on…

用于不完整结果数据估计的协变量平衡稳定权重AI 解读

Weighting methods that adjust for observed covariates, such as inverse probability weighting, are widely used for causal inference and estimation with incomplete outcome data. Part of the appeal of such methods is that one set of weights can be used to estimate a range of treatment effects based on different outcomes, or a variety of population means for several variables. However, this appeal can be diminished in practice by the instability of the estimated weights and by…

边际结构模型中逆概率权重的稳健估计AI 解读

Marginal structural models (MSMs) are becoming increasingly popular as a tool for causal inference from longitudinal data. Unlike standard regression models, MSMs can adjust for time-dependent observed confounders while avoiding the bias due to the direct adjustment for covariates affected by the treatment. Despite their theoretical appeal, a main practical difficulty of MSMs is the required estimation of inverse probability weights. Previous studies have found that MSMs can…

偏差缩减的双重稳健估计AI 解读

Over the past decade, doubly robust estimators have been proposed for a variety of target parameters in causal inference and missing data models. These are asymptotically unbiased when at least one of two nuisance working models is correctly specified, regardless of which. While their asymptotic distribution is not affected by the choice of root-n consistent estimators of the nuisance parameters indexing these working models when all working models are correctly specified,…

二元三角系统中处理效应的尖锐界限AI 解读

This paper considers the evaluation of the average treatment effect (ATE) in a triangular system with binary dependent variables. I impose a threshold crossing model on both the endogenous regressor and the outcome. The bounds proposed by Shaikh and Vytlacil (2011,SV) on the ATE are sharp only under a restrictive condition on the support of the covariates and the instruments, which rules out a wide range of models and many relevant applications. In this setting, I provide a…

谁从环境监管中受益？来自《清洁空气法》修正案的证据AI 解读

Using geographically disaggregated data and exploiting an instrumental variable strategy, we show that contrary to conventional wisdom, the benefits of the 1990 Clean Air Act Amendments (CAAA) were progressive. The CAAA created incentives for local regulators to target the initially dirtiest areas for cleanup, creating heterogeneity in the incidence of air quality improvements that favored lower-income households. Based on house price appreciation, households in the lowest…

多水平干预与多分量中介下的因果中介分析AI 解读

Summary Mediation analysis is an important tool in social and medical sciences as it helps to understand why an intervention works. The commonly used approach, given by Baron and Kenny, requires the strong assumption ‘sequential ignorability’ to yield causal interpretation. Ten Have and his colleagues proposed a rank preserving model to relax this assumption. However, the rank preserving model is restricted to the case with binary intervention and single mediator and needs…

回归不连续设计的经验似然推断AI 解读

This paper proposes empirical likelihood based inference methods for causal effects identified from regression discontinuity designs. We consider both the sharp and fuzzy regression discontinuity designs and treat the regression functions as nonparametric. The proposed inference procedures do not require asymptotic variance estimation and the confidence sets have natural shapes, unlike the conventional Wald-type method. These features are illustrated by simulations and an…

次贷危机应归咎于政府住房政策吗？AI 解读

Some have suggested that housing policy, embodied by the Community Reinvestment Act (CRA) and affordable housing goals of the government-sponsored enterprises (GSEs), caused the subprime crisis. We examine if these programs led to worse mortgage outcomes using two approaches. The first examines whether more activity by CRA-covered lenders, or more loan sales to the GSEs, was associated with worse outcomes. The second uses regression discontinuity to determine if outcomes…

处理水平众多而工具变量有限时的模型估计与检验AI 解读

Empirical researchers interested in the causal effect of the endogenous regressor often use instrumental variables. When few valid instruments are available, they typically estimate restricted specifications that impose uniform per unit treatment effects, even when these effects are likely to vary. We show that in these cases, ordinary least squares and instrumental variables estimators identify different weighted averages of all per unit effects, so the traditional Hausman…

基于不等式矩约束检验LATE识别中的工具变量有效性AI 解读

We derive testable implications of instrument validity in just identified treatment effect models with endogeneity and consider several tests. The identifying assumptions of the local average treatment effect allow us to both point identify and bound the mean potential outcomes of the always takers under treatment and the never takers under nontreatment. The point-identified means must lie within their respective bounds, which provides us with four testable inequality moment…

小支撑集工具变量下非可分模型的识别AI 解读

I consider nonparametric identification of nonseparable instrumental variables models with continuous endogenous variables. If both the outcome and first stage equations are strictly increasing in a scalar unobservable, then many kinds of continuous, discrete, and even binary instruments can be used to point-identify the levels of the outcome equation. This contrasts sharply with related work by Imbens and Newey, 2009 that requires continuous instruments with large support.…

新外科医生健康结局观察研究中的精细协变量平衡与大规模稀疏最优匹配AI 解读

Every newly trained surgeon performs her first unsupervised operation. How do the health outcomes of her patients compare with the patients of experienced surgeons? Using data from 498 hospitals, we compare 1252 pairs comprised of a new surgeon and an experienced surgeon working at the same hospital. We introduce a new form of matching that matches patients of each new surgeon to patients of an otherwise similar experienced surgeon at the same hospital, perfectly balancing…

估计最优动态治疗方案的新统计学习方法AI 解读

Dynamic treatment regimes (DTRs) are sequential decision rules for individual patients that can adapt over time to an evolving illness. The goal is to accommodate heterogeneity among patients and find the DTR which will produce the best long-term outcome if implemented. We introduce two new statistical learning methods for estimating the optimal DTR, termed backward outcome weighted learning (BOWL), and simultaneous outcome weighted learning (SOWL). These approaches convert…

协变量自适应随机临床试验的假设检验AI 解读

Covariate-adaptive designs are often implemented to balance important covariates in clinical trials. However, the theoretical properties of conventional testing hypotheses are usually unknown under covariate-adaptive randomized clinical trials. In the literature, most studies are based on simulations. In this article, we provide theoretical foundation of hypothesis testing under covariate-adaptive designs based on linear models. We derive the asymptotic distributions of the…

工资差距分解中的因果陷阱AI 解读

The decomposition of gender or ethnic wage gaps into explained and unexplained components (often with the aim to assess labor market discrimination) has been a major research agenda in empirical labor economics. This article demonstrates that conventional decompositions, no matter whether linear or nonparametric, are equivalent to assuming a (probably too) simple model of mediation (aimed at assessing causal mechanisms) and may therefore lack causal interpretability. The…

广义Roy模型与社会项目的成本收益分析AI 解读

The literature on treatment effects focuses on gross benefits from program participation. We extend this literature by developing conditions under which it is possible to identify parameters measuring the cost and net surplus from program participation. Using the generalized Roy model, we nonparametrically identify the cost, benefit, and net surplus of selection into treatment without requiring the analyst to have direct information on costs. We apply our methodology to…

格点过程的模型设定检验AI 解读

We consider an omnibus test for the correct specification of the dynamics of a sequence $\left\{ {x\left( t \right)} \right\}_{t \in Z^d } $ in a lattice. As it happens with causal models and d = 1, its asymptotic distribution is not pivotal and depends on the estimator of the unknown parameters of the model under the null hypothesis. One first main goal of the paper is to provide a transformation to obtain an asymptotic distribution that is free of nuisance parameters.…