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This article develops a framework for estimating multivariate treatment effect models in the presence of sample selection. The methodology deals with several important issues prevalent in policy and program evaluation, including application and approval stages, nonrandom treatment assignment, endogeneity, and discrete outcomes. This article presents a computationally efficient estimation algorithm and techniques for model comparison and treatment effects. The framework is…

This article provides a strategy to identify the existence and direction of a causal effect in a generalized nonparametric and nonseparable model identified by instrumental variables. The causal effect concerns how the outcome depends on the endogenous treatment variable. The outcome variable, treatment variable, other explanatory variables, and the instrumental variable can be essentially any combination of continuous, discrete, or “other” variables. In particular, it is…

可能非唯一最优治疗策略下平均结局的统计推断AI 解读

We consider challenges that arise in the estimation of the mean outcome under an optimal individualized treatment strategy defined as the treatment rule that maximizes the population mean outcome, where the candidate treatment rules are restricted to depend on baseline covariates. We prove a necessary and sufficient condition for the pathwise differentiability of the optimal value, a key condition needed to develop a regular and asymptotically linear (RAL) estimator of the…

多维聚合函数的识别与估计：福祉分位前沿应用AI 解读

We propose a new technique for identification and estimation of aggregation functions in multidimensional evaluations and multiple indicator settings. These functions may represent “latent” objects. They occur in many different contexts, for instance in propensity scores, multivariate measures of well-being and the related analysis of inequality and poverty, and in equivalence scales. Technical advances allow nonparametric inference on the joint distribution of continuous…

动态处理效应AI 解读

This paper develops robust models for estimating and interpreting treatment effects arising from both ordered and unordered multi-stage decision problems. Identification is secured through instrumental variables and/or conditional independence (matching) assumptions. We decompose treatment effects into direct effects and continuation values associated with moving to the next stage of a decision problem. Using our framework, we decompose the IV estimator, showing that IV…

利用不平等指标变化识别与估计干预的分布影响AI 解读

This paper presents estimators of distributional impacts of interventions when selection to the program is based on observable characteristics. Distributional impacts are calculated as differences in inequality measures of the marginal distributions of potential outcomes of receiving and not receiving the treatment. The estimation procedure involves a first non-parametric estimation of the propensity score. In the second step weighted versions of inequality measures are…

不可忽略缺失数据的半参数逆倾向加权AI 解读

Abstract To estimate unknown population parameters based on data having nonignorable missing values with a semiparametric exponential tilting propensity, Kim & Yu (2011) assumed that the tilting parameter is known or can be estimated from external data, in order to avoid the identifiability issue. To remove this serious limitation on the methodology, we use an instrument, i.e., a covariate related to the study variable but unrelated to the missing data propensity, to…

随机机制交互作用AI 解读

We define mechanistic interaction between the effects of two variables on an outcome in terms of departure of these effects from a generalized noisy-OR model in a stratum of the population. We develop a fully probabilistic framework for the observational identification of this type of interaction via excess risk or superadditivity, one novel feature of which is its applicability when the interacting variables have been generated by arbitrarily dichotomizing continuous…

结构向量自回归过程的贝叶斯图模型AI 解读

Summary This paper proposes a Bayesian, graph‐based approach to identification in vector autoregressive (VAR) models. In our Bayesian graphical VAR (BGVAR) model, the contemporaneous and temporal causal structures of the structural VAR model are represented by two different graphs. We also provide an efficient Markov chain Monte Carlo algorithm to estimate jointly the two causal structures and the parameters of the reduced‐form VAR model. The BGVAR approach is shown to be…

用于工具变量选择的Lasso：一项复现研究AI 解读

Summary Recently, Lasso methods have been applied to economic questions. In a seminal paper, Belloni et al . ( Econometrica ; 80 (6): 2369–2429) make use of (post‐)Lasso for instrumental variable selection in a setting where the number of instruments p is large or might even exceed the number of observations n —a situation which is prevalent in many current applications. We replicate their simulation study with the statistical package R (R Development Core Team ( )) and,…

In many statistical applications of one-sided tests of multiple hypotheses researchers are often concerned not only with global tests of the intersection of individual hypotheses, but also with multiple tests of individual hypotheses. For example, in clinical trial studies researchers often need to find out the efficacy of a treatment, as well as the significance of each outcome measurement (endpoint) of the treatment. This article proposes MaxT type tests aiming at…

部分工具变量无效时的工具变量估计及孟德尔随机化应用AI 解读

Instrumental variables have been widely used for estimating the causal effect between exposure and outcome. Conventional estimation methods require complete knowledge about all the instruments’ validity; a valid instrument must not have a direct effect on the outcome and not be related to unmeasured confounders. Often, this is impractical as highlighted by Mendelian randomization studies where genetic markers are used as instruments and complete knowledge about instruments’…

Causal inference in completely randomized treatment-control studies with binary outcomes is discussed from Fisherian, Neymanian, and Bayesian perspectives, using the potential outcomes model. A randomization-based justification of Fisher’s exact test is provided. Arguing that the crucial assumption of constant causal effect is often unrealistic, and holds only for extreme cases, some new asymptotic and Bayesian inferential procedures are proposed. The proposed procedures…

序贯条件独立下中介效应估计量的有限样本表现AI 解读

Using a comprehensive simulation study based on empirical data, this article investigates the finite sample properties of different classes of parametric and semiparametric estimators of (natural) direct and indirect causal effects used in mediation analysis under sequential conditional independence assumptions. The estimators are based on regression, inverse probability weighting, and combinations thereof. Our simulation design uses a large population of Swiss jobseekers…

医疗支出价格弹性的删失分位数工具变量估计AI 解读

Efforts to control medical care costs depend critically on how individuals respond to prices. I estimate the price elasticity of expenditure on medical care using a censored quantile instrumental variable (CQIV) estimator. CQIV allows estimates to vary across the conditional expenditure distribution, relaxes traditional censored model assumptions, and addresses endogeneity with an instrumental variable. My instrumental variable strategy uses a family member's injury to…

死亡截断问题中利用结局测量后的信息AI 解读

Summary Many clinical studies on non-mortality outcomes such as quality of life suffer from the problem that the non-mortality outcome can be censored by death, i.e. the non-mortality outcome cannot be measured if the subject dies before the time of measurement. To address the problem that this censoring by death is informative, it is of interest to consider the average effect of the treatment on the non-mortality outcome among subjects whose measurement would not be…

股票回购的实体经济效应AI 解读

We employ a regression discontinuity design to identify the real effects of share repurchases on other firm outcomes. The probability of share repurchases that increase earnings per share (EPS) is sharply higher for firms that would have just missed the EPS forecast in the absence of the repurchase, when compared with firms that “just beat” the EPS forecast. We use this discontinuity to show that EPS-motivated repurchases are associated with reductions in employment and…

股票回购与流动性AI 解读

We analyze the impact of share repurchases on liquidity based on a new comprehensive data set of realized share repurchases in the US, which covers 50,204 repurchase months between 2004 and 2010. Using instrumental variable analysis, we show that repurchases unequivocally improve liquidity and suggest that endogenous controls have confounded results in earlier studies. Liquidity also influences how firms execute repurchase programs. Repurchases provide liquidity when other…

基于估计倾向得分的匹配AI 解读

Propensity score matching estimators (Rosenbaum and Rubin, 1983) are widely used in evaluation research to estimate average treatment effects. In this article, we derive the large sample distribution of propensity score matching estimators. Our derivations take into account that the propensity score is itself estimated in a first step, prior to matching. We prove that first step estimation of the propensity score affects the large sample distribution of propensity score…

组层面处理的工具变量分位数回归：贸易的分布效应AI 解读

We present a methodology for estimating the distributional effects of an endogenous treatment that varies at the group level when there are group-level unobservables, a quantile extension of Hausman and Taylor, 1981. Because of the presence of group-level unobservables, standard quantile regression techniques are inconsistent in our setting even if the treatment is independent of unobservables. In contrast, our estimation technique is consistent as well as computationally…

夜幕掩护之下：环境光照如何影响犯罪活动AI 解读

Abstract We exploit daylight saving time (DST) as an exogenous shock to daylight, using both the discontinuous nature of the policy and the 2007 extension of DST, to consider the impact of light on criminal activity. Regression discontinuity estimates show a 7% decrease in robberies following the shift to DST. As expected, effects are largest during the hours directly affected by the shift in daylight. We discuss our findings within the context of criminal decision making…

Lévy 驱动连续时间 ARMA 过程的预测AI 解读

The conditional expectations, E(Y(h)|Y(u),−∞<u≤0) and E(Y(h)|Y(u),−M≤u≤0) with h>0 and 0<M<∞ are determined for a continuous-time ARMA (CARMA) process (Y(t))t∈R driven by a Lévy process L with E|L(1)|<∞. If E(L(1)2)<∞ these are the minimum mean-squared error predictors of Y(h) given (Y(t))t≤0 and (Y(t))−M≤t≤0 respectively. Conditions are also established under which the sample-path of L can be recovered from that of Y, both when Y is causal and strictly stationary and…

债务与税收：利用州税变化估计企业杠杆的税收敏感性AI 解读

Using staggered corporate income tax changes across U.S. states, we show that taxes have a first-order effect on capital structure. Firms increase leverage by around 40 basis points for every percentage-point tax increase. Consistent with dynamic tradeoff theory, the effect is asymmetric: leverage does not respond to tax cuts. This is true even within-firm: tax increases that are later reversed nonetheless lead to permanent leverage increases. The treatment effects are…

治疗分配与终止相竞争时治疗下平均结局的直接估计AI 解读

Several authors have investigated the challenges of statistical analyses and inference in the presence of early treatment termination, including a loss of efficiency in randomized controlled trials and a connection to dynamic regimes in observational studies. Popular estimation strategies for causal estimands in dynamic regimes lend themselves to studies where treatment is assigned at a finite number of points and the extension to continuous treatment assignment is…