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处理效应半参数估计中的模型平均AI 解读

Choosing the covariates and functional form of the propensity score is an important choice for estimating treatment effects. This paper proposes a data-driven way of averaging the estimators over candidate specifications to resolve the specification uncertainty in the propensity score weighting estimation of the ATT. The proposed procedures minimize the estimated MSE of the ATT estimator in a local asymptotic framework. We formulate model averaging as a statistical decision…

面板结果的贝叶斯处理效应与变量选择：产假对收入的影响AI 解读

We propose two alternative Bayesian treatment effect modeling and inferential frameworks for panel outcomes to estimate dynamic earnings effects of a long maternity leave on mothers’ subsequent earnings. Modeling of the endogeneity of the treatment and the panel structure of the earnings are based on the modeling tradition of the Roy switching regression model and the shared factor approach, respectively. We implement stochastic variable selection to test, for example, for…

区域政策评估：交互固定效应与合成控制AI 解读

In this paper, we investigate the use of interactive effect or linear factor models in regional policy evaluation. We contrast treatment effect estimates obtained using Bai (2009) with those obtained using difference in differences and synthetic controls (Abadie and coauthors). We show that difference in differences are generically biased, and we derive support conditions for synthetic controls. We construct Monte Carlo experiments to compare these estimation methods in…

通过经验平衡校准加权实现平均处理效应的全局有效非参数推断AI 解读

Summary The estimation of average treatment effects based on observational data is extremely important in practice and has been studied by generations of statisticians under different frameworks. Existing globally efficient estimators require non-parametric estimation of a propensity score function, an outcome regression function or both, but their performance can be poor in practical sample sizes. Without explicitly estimating either function, we consider a wide class of…

处理效应变异的随机化推断AI 解读

Summary Applied researchers are increasingly interested in whether and how treatment effects vary in randomized evaluations, especially variation that is not explained by observed covariates. We propose a model-free approach for testing for the presence of such unexplained variation. To use this randomization-based approach, we must address the fact that the average treatment effect, which is generally the object of interest in randomized experiments, actually acts as a…

使用回归不连续设计评估GED证书的劳动力市场回报AI 解读

We evaluate returns to General Educational Development (GED) certification for high school dropouts using state administrative data. We apply a fuzzy regression discontinuity method to account for test takers retaking the test. For women we find that GED certification has no statistically significant effect on either employment or earnings. For men we find a significant increase in earnings in the second year after taking the test but no impact in subsequent years. GED…

良好信用声誉的价值：来自信用卡债务重谈的证据AI 解读

I exploit a natural experiment to estimate borrowers’ willingness to pay for a good credit reputation. A lender in Chile offered lower installments to borrowers who were in default. Those who owed more than a fixed arbitrary cutoff were additionally offered a clean public repayment record. Using the cutoff in a fuzzy regression discontinuity design, I show that borrowers are willing to pay the equivalent of 11% of their monthly income for a good reputation. Borrowers use…

未测中介—结局混杂下中介效应的锐敏感性界限AI 解读

It is often of interest to decompose the total effect of an exposure into a component that acts on the outcome through some mediator and a component that acts independently through other pathways. Said another way, we are interested in the direct and indirect effects of the exposure on the outcome. Even if the exposure is randomly assigned, it is often infeasible to randomize the mediator, leaving the mediator-outcome confounding not fully controlled. We develop a…

整群抽样调查无应答的校准倾向评分方法AI 解读

Weighting adjustment is commonly used in survey sampling to correct for unit nonresponse. In cluster sampling, the missingness indicators are often correlated within clusters and the response mechanism is subject to cluster-specific nonignorable missingness. Based on a parametric working model for the response mechanism that incorporates cluster-specific nonignorable missingness, we propose a method of weighting adjustment. We provide a consistent estimator of the mean or…

含二元内生解释变量的非参数三角模型估计AI 解读

We consider identification and estimation in a nonparametric triangular system with a binary endogenous regressor and nonseparable errors. For identification, we take a control function approach utilizing the Dynkin system idea. We articulate various trade‐offs, including continuity, monotonicity and differentiability. For estimation, we use the idea of local instruments under smoothness assumptions, but we do not assume additive separability in latent variables. Our…

具有一个排除限制的有限混合模型AI 解读

This paper characterizes the identified set for finite mixture models with one exclusion restriction on the data‐generating process. It provides necessary and sufficient conditions on the observed data for point identification and for when the identified set has measure zero. The results are illustrated in a simulation study and with data from a randomized controlled trial on chemotherapy for colon cancer as well as with data from an observational study used to estimate…

朋友与邻居：局部经济发展与犯罪活动AI 解读

Abstract We exploit a sudden shock to demand for a subset of low-wage workers generated by the 2005 Base Realignment and Closure (BRAC) program in San Antonio, Texas, to identify the effects of localized economic development on crime. We use a difference-in-differences methodology that takes advantage of variation in BRAC’s impact over time and across neighborhoods. We find that appropriative criminal behavior increases in neighborhoods where a fraction of residents…

混频数据的格兰杰因果检验AI 解读

We develop Granger causality tests that apply directly to data sampled at different frequencies. We show that taking advantage of mixed frequency data allows us to better recover causal relationships when compared to the conventional common low frequency approach. We also show that the new causality tests have higher local asymptotic power as well as more power in finite samples compared to conventional tests. In an empirical application involving U.S. macroeconomic…

银行的地理扩张能否降低风险？AI 解读

We develop a new identification strategy to evaluate the impact of the geographic expansion of a bank holding company (BHC) across US metropolitan statistical areas (MSAs) on BHC risk. For the average BHC, the instrumental variable results suggest that geographic expansion materially reduces risk. Geographic diversification does not affect loan quality. The results are consistent with arguments that geographic expansion lowers risk by reducing exposure to idiosyncratic local…

华尔街的旋转门AI 解读

Credit analysts often leave rating agencies to work at firms they rate. We use benchmark rating agencies as counterfactuals to measure rating inflation in a difference-in-differences framework and find that transitioning analysts award inflated ratings to their future employers before switching jobs. We find no evidence that analysts inflate ratings of other firms they rate. Market based measures of hiring firms' credit quality further indicate that transitioning analysts'…

司机请注意，附近有儿童：交通、污染与婴儿健康AI 解读

We investigate the effects of automobile congestion on ambient air pollution and local infant mortality rates using data from California spanning 2002 to 2007. Constructing instrumental variables (IV) using the relationship of traffic, weather conditions, and pollutants, we show that particulate matter, even at modern levels, has large marginal effects on weekly infant mortality rates, especially for premature or low birthweight infants. We also find suggestive evidence of…

利用纵向盲法评估联合估计治疗效应与安慰剂效应AI 解读

In some therapeutic areas, treatment evaluation is frequently complicated by a possible placebo effect (i.e., the psychobiological effect of a patient's knowledge or belief of being treated). When a substantial placebo effect is likely to exist, it is important to distinguish the treatment and placebo effects in quantifying the clinical benefit of a new treatment. These causal effects can be formally defined in a joint causal model that includes treatment (e.g., new versus…

重症脓毒症死亡率观察研究：可解释研究人群与随机化推断的离散优化AI 解读

Motivated by an observational study of the effect of hospital ward versus intensive care unit admission on severe sepsis mortality, we develop methods to address two common problems in observational studies: (1) when there is a lack of covariate overlap between the treated and control groups, how to define an interpretable study population wherein inference can be conducted without extrapolating with respect to important variables; and (2) how to use randomization inference…

聚类鼓励设计中因果机制的识别与估计：用贝叶斯主分层分解蚊帐干预效应AI 解读

Exploration of causal mechanisms is often important for researchers and policymakers to understand how an intervention works and how it can be improved. This task can be crucial in clustered encouragement designs (CED). Encouragement design studies arise frequently when the treatment cannot be enforced because of ethical or practical constrains and an encouragement intervention (information campaigns, incentives, etc) is conceived with the purpose of increasing the uptake of…

面向个体化医疗的主动临床试验AI 解读

Individualized treatment rules (ITRs) tailor treatments according to individual patient characteristics. They can significantly improve patient care and are thus becoming increasingly popular. The data collected during randomized clinical trials are often used to estimate the optimal ITRs. However, these trials are generally expensive to run, and, moreover, they are not designed to efficiently estimate ITRs. In this article, we propose a cost-effective estimation method from…

纵向数据半参数协方差分析模型的广义准似然比检验AI 解读

We model generalized longitudinal data from multiple treatment groups by a class of semiparametric analysis of covariance models, which take into account the parametric effects of time dependent covariates and the nonparametric time effects. In these models, the treatment effects are represented by nonparametric functions of time and we propose a generalized quasi-likelihood ratio test procedure to test if these functions are identical. Our estimation procedure is based on…

存在不可观测异质性的处理效应：集合识别方法AI 解读

We propose the sharp identifiable bounds of the potential outcome distributions using panel data. We allow for the possibility that statistical randomization of treatment assignments is not achieved until unobserved heterogeneity is properly controlled for. We use certain stationarity assumptions to obtain the sharp bounds. Our approach allows for dynamic treatment decisions, where the current treatment decisions may depend on the past treatments or the past observed…

通过辅助样本向研究样本倾斜法（AST）有效估计数据组合模型AI 解读

We propose a locally efficient estimator for a class of semiparametric data combination problems. A leading estimand in this class is the average treatment effect on the treated (ATT). Data combination problems are related to, but distinct from, the class of missing data problems with data missing at random (of which the average treatment effect (ATE) estimand is a special case). Our estimator also possesses a double robustness property. Our procedure may be used to…

In fuzzy regression discontinuity (FRD) designs, the treatment effect is identified through a discontinuity in the conditional probability of treatment assignment. We show that when identification is weak (i.e., when the discontinuity is of a small magnitude), the usual t-test based on the FRD estimator and its standard error suffers from asymptotic size distortions as in a standard instrumental variables setting. This problem can be especially severe in the FRD setting…