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The identification power of smoothness assumptions in models with counterfactual outcomes

反事实结果模型中平滑性假设的识别能力

In this paper, we investigate what can be learned about average counterfactual outcomes as well as average treatment effects when it is assumed that treatment response functions are smooth. We obtain a set of new partial identification results for both the average treatment response and the average treatment effect. In particular, we find that the monotone treatment response and monotone treatment selection bound of Manski and Pepper, 2000 can be further tightened if we…

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Measuring mobility

流动性的测度

Our new approach to mobility measurement involves separating out the valuation of positions in terms of individual status (using income, social rank, or other criteria) from the issue of movement between positions. The quantification of movement is addressed using a general concept of distance between positions and a parsimonious set of axioms that characterize the distance concept and yield a class of aggregative indices. This class of indices induces a superclass of…

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Information structure and statistical information in discrete response models

离散响应模型中的信息结构与统计信息

Strategic interaction parameters characterize the impact of actions of one economic agent on the payoff of another economic agent, and are of great interest in both theoretical and empirical work. In this paper, by considering econometric models involving simultaneous discrete systems of equations, we study how the information available to economic agents regarding other economic agents can influence whether or not these strategic information parameters can be inferred from…

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On two-stage estimation of structural instrumental variable models

结构工具变量模型的两阶段估计

Two-stage least squares estimation is popular for structural equation models with unmeasured confounders. In such models, both the outcome and the exposure are assumed to follow linear models conditional on the measured confounders and instrumental variable, which is related to the outcome only via its relation with the exposure. We consider data where both the outcome and the exposure may be incompletely observed, with particular attention to the case where both are…

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Doubly robust nonparametric inference on the average treatment effect

平均处理效应的双重稳健非参数推断

Summary Doubly robust estimators are widely used to draw inference about the average effect of a treatment. Such estimators are consistent for the effect of interest if either one of two nuisance parameters is consistently estimated. However, if flexible, data-adaptive estimators of these nuisance parameters are used, double robustness does not readily extend to inference. We present a general theoretical study of the behaviour of doubly robust estimators of an average…

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Differential network analysis via lasso penalized D-trace loss

基于 lasso 惩罚 D-trace 损失的差异网络分析

Biological networks often change under different environmental and genetic conditions. In this paper, we model network change as the difference of two precision matrices and propose a novel loss function called the D-trace loss, which allows us to directly estimate the precision matrix difference without attempting to estimate the precision matrices themselves. Under a new irrepresentability condition, we show that the D-trace loss function with the lasso penalty can yield…

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A $C_p$ criterion for semiparametric causal inference

半参数因果推断中的 Cp 模型选择准则

Summary For marginal structural models, which play an important role in causal inference, we consider a model selection problem within a semiparametric framework using inverse-probability-weighted estimation or doubly robust estimation. In this framework, the modelling target is a potential outcome that may be missing, so there is no classical information criterion. We define a mean squared error for treating the potential outcome and derive an asymptotic unbiased estimator…

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Estimating network edge probabilities by neighbourhood smoothing

通过邻域平滑估计网络连边概率

Summary The estimation of probabilities of network edges from the observed adjacency matrix has important applications to the prediction of missing links and to network denoising. It is usually addressed by estimating the graphon, a function that determines the matrix of edge probabilities, but this is ill-defined without strong assumptions on the network structure. Here we propose a novel computationally efficient method, based on neighbourhood smoothing, to estimate the…

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Extended conditional independence and applications in causal inference

扩展条件独立及其在因果推断中的应用

The goal of this paper is to integrate the notions of stochastic conditional independence and variation conditional independence under a more general notion of extended conditional independence. We show that under appropriate assumptions the calculus that applies for the two cases separately (axioms of a separoid) still applies for the extended case. These results provide a rigorous basis for a wide range of statistical concepts, including ancillarity and sufficiency, and,…

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Nonparametric goodness-of-fit tests for uniform stochastic ordering

一致随机序的非参数拟合优度检验

We propose $L^{p}$ distance-based goodness-of-fit (GOF) tests for uniform stochastic ordering with two continuous distributions $F$ and $G$, both of which are unknown. Our tests are motivated by the fact that when $F$ and $G$ are uniformly stochastically ordered, the ordinal dominance curve $R=FG^{-1}$ is star-shaped. We derive asymptotic distributions and prove that our testing procedure has a unique least favorable configuration of $F$ and $G$ for $p\in [1,\infty]$. We use…

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Targeted sequential design for targeted learning inference of the optimal treatment rule and its mean reward

最优治疗规则及其平均回报推断的靶向序贯设计

, assuming that there is no stratum of the baseline covariates where treatment is neither beneficial nor harmful, and under a companion margin assumption. Our pivotal estimator, whose definition hinges on the targeted minimum loss estimation (TMLE) principle, actually infers the mean reward under the current estimate of the optimal TR. This data-adaptive statistical parameter is worthy of interest on its own. Our main result is a central limit theorem which enables the…

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Rationalization and identification of binary games with correlated types

私人类型相关的二元博弈:模型相容性与识别

This paper studies the rationalization and identification of binary games where players have correlated private types. Allowing for type correlation is crucial in global games and in models with social interactions as it represents correlated private information and homophily, respectively. Our approach is fully nonparametric in the joint distribution of types and the strategic effects in the payoffs. First, under monotone pure Bayesian Nash Equilibrium strategy, we…

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Volatile Top Income Shares in Switzerland? Reassessing the Evolution between 1981 and 2010

瑞士顶端收入份额波动吗?重新评估1981—2010年的演变

In the past twenty years, the share of top incomes in Switzerland has risen, while exhibiting large variations. Switzerland is similar to European countries for the top 1% but closer to the United States for higher top income groups. With the synthetic control method, we close a time gap in the tax data, exploiting the fact that Swiss cantons changed their tax system at different points in time. Using social security data, which cover all top labor incomes, we document the…

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INTEGRATED SCORE ESTIMATION

积分得分估计

We study the properties of the integrated score estimator (ISE), which is the Laplace version of Manski’s maximum score estimator (MMSE). The ISE belongs to a class of estimators whose basic asymptotic properties were studied in Jun, Pinkse, and Wan (2015, Journal of Econometrics 187(1), 201–216). Here, we establish that the MMSE, or more precisely $$\root 3 \of n |\hat \theta _M - \theta _0 |$$ , (locally first order) stochastically dominates the ISE under the conditions…

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SEMIPARAMETRIC ESTIMATION OF RANDOM COEFFICIENTS IN STRUCTURAL ECONOMIC MODELS

结构经济模型中随机系数的半参数估计

This paper discusses nonparametric estimation of the distribution of random coefficients in a structural model that is nonlinear in the random coefficients. We establish that the problem of recovering the probability density function ( pdf ) of random parameters falls into the class of convexly-constrained inverse problems. The framework offers an estimation method that separates computational solution of the structural model from estimation. We first discuss nonparametric…

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Sparse Graphs Using Exchangeable Random Measures

利用可交换随机测度构造稀疏图

Summary Statistical network modelling has focused on representing the graph as a discrete structure, namely the adjacency matrix. When assuming exchangeability of this array—which can aid in modelling, computations and theoretical analysis—the Aldous–Hoover theorem informs us that the graph is necessarily either dense or empty. We instead consider representing the graph as an exchangeable random measure and appeal to the Kallenberg representation theorem for this object. We…

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Direct and Indirect Treatment Effects–Causal Chains and Mediation Analysis with Instrumental Variables

直接与间接处理效应:工具变量下的因果链与中介分析

Summary The paper discusses the non-parametric identification of causal direct and indirect effects of a binary treatment based on instrumental variables. We identify the indirect effect, which operates through a mediator (i.e. intermediate variable) that is situated on the causal path between the treatment and the outcome, as well as the unmediated direct effect of the treatment by using distinct instruments for the endogenous treatment and the endogenous mediator. We…

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Concordance-Assisted Learning for Estimating Optimal Individualized Treatment Regimes

用于估计最优个体化治疗规则的一致性辅助学习

Summary We propose new concordance-assisted learning for estimating optimal individualized treatment regimes. We first introduce a type of concordance function for prescribing treatment and propose a robust rank regression method for estimating the concordance function. We then find treatment regimes, up to a threshold, to maximize the concordance function, named the prescriptive index. Finally, within the class of treatment regimes that maximize the concordance function, we…

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Robust Estimation of Encouragement Design Intervention Effects Transported Across Sites

跨站点迁移鼓励设计干预效应的稳健估计

Summary We develop robust targeted maximum likelihood estimators (TMLEs) for transporting intervention effects from one population to another. Specifically, we develop TMLEs for three transported estimands: the intent-to-treat average treatment effect (ATE) and complier ATE, which are relevant for encouragement design interventions and instrumental variable analyses, and the ATE of the exposure on the outcome, which is applicable to any randomized or observational study. We…

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Regression discontinuity with categorical outcomes

分类结果变量的回归不连续设计

We consider the regression discontinuity (RD) design with categorical outcomes, and exploit the possibility of adapting well-developed microeconometric models to the RD setting. The channels through which the forcing variable affects the potential outcome distributions are constrained to be minimal, to preserve the nonparametric feature of the RD design. Focusing on general categorical outcomes (nominal or ordinal), we develop a new RD estimator based on a nonparametric…

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Nonparametric conditional quantile estimation: A locally weighted quantile kernel approach

非参数条件分位数估计:局部加权分位数核方法

Nonparametric conditional cumulative distribution function (CDF) estimation has emerged as a powerful tool having widespread potential application, which has led to a literature on estimators of conditional quantile functions that are obtained via inversion of the nonparametrically estimated conditional CDF. Other nonparametric estimators of conditional quantiles that are based on an alternative characterization of the quantile (i.e., as the function that minimizes the…

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The triangular model with random coefficients

随机系数三角模型

The triangular model is a very popular way to allow for causal inference in the presence of endogeneity. In this model, an outcome is determined by an endogenous regressor, which in turn is first caused by an instrument. We study the triangular model with random coefficients and additional exogenous regressors in both equations, and establish non-identification of the joint distribution of random coefficients. This implies that counterfactual outcomes are not identified…

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Doubly robust uniform confidence band for the conditional average treatment effect function

条件平均处理效应函数的双重稳健一致置信带

Summary In this paper, we propose a doubly robust method to estimate the heterogeneity of the average treatment effect with respect to observed covariates of interest. We consider a situation where a large number of covariates are needed for identifying the average treatment effect but the covariates of interest for analyzing heterogeneity are of much lower dimension. Our proposed estimator is doubly robust and avoids the curse of dimensionality. We propose a uniform…

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Estimating the economic costs of organized crime by synthetic control methods

用合成控制法估计有组织犯罪的经济成本

Summary The economic costs of organized crime have been estimated for the case of southern Italy by Pinotti ( Economic Journal 2015; 125 , F203–F232, 2015): using synthetic control methods, he finds that, due to the advent of the Italian Mafia in the regions Apulia and Basilicata, GDP per capita dropped by 16%. Replicating this study in a narrow sense by estimating the same model with the same data, but using different software implementations, we observe minor differences…

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