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A General Dynamical Statistical model with Causal Interpretation

Summary We develop a general dynamical model as a framework for causal interpretation. We first state a criterion of local independence in terms of measurability of processes that are involved in the Doob–Meyer decomposition of stochastic processes; then we define direct and indirect influence. We propose a definition of causal influence using the concepts of a ‘physical system’. This framework makes it possible to link descriptive and explicative statistical models, and…

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Intervention and Causality: Forecasting Traffic Flows Using a Dynamic Bayesian Network

干预与因果关系:利用动态贝叶斯网络预测交通流

Real-time traffic flow data across entire networks can be used in a traffic management system to monitor current traffic flows so that traffic can be directed and managed efficiently. Reliable short-term forecasting models of traffic flows are crucial for the success of any traffic management system. The model proposed in this article for forecasting traffic flows is a multivariate Bayesian dynamic model called the multiregression dynamic model (MDM). This model is an…

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Doubly Robust Internal Benchmarking and False Discovery Rates for Detecting Racial Bias in Police Stops

识别警察拦截中种族偏差的双重稳健内部基准与错误发现率

Allegations of racially biased policing are a contentious issue in many communities. Processes that flag potential problem officers have become a key component of risk management systems at major police departments. We present a statistical method to flag potential problem officers by blending three methodologies that are the focus of active research efforts: propensity score weighting, doubly robust estimation, and false discovery rates. Compared with other systems…

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Sensitivity Analysis for Equivalence and Difference in an Observational Study of Neonatal Intensive Care Units

新生儿重症监护观察研究中等效与差异检验的敏感性分析

In randomized experiments, it is sometimes important to demonstrate that two treatments do not differ greatly in their effects, or to demonstrate that the treatments have very different effects on one outcome but similar effects on another outcome. These “demonstrations” may take the form of rejecting a null hypothesis of inequivalence in an equivalence test, or rejecting a null hypothesis of equal and inequivalent effects on two outcomes. The procedures often express a…

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Inferring Optimal Peer Assignment From Experimental Data

从实验数据推断最优同伴分配

This article studies the problem of optimally dividing individuals into peer groups to maximize social gains from heterogeneous peer effects. The specific setting analyzed here concerns efficient ways of allocating roommates in college dormitories. Using confidential data on a sample of Dartmouth College freshmen who were randomly assigned to be roommates, the article derives efficient roommate pairing rules, based on demographic and academic background, which maximize…

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Identification of peer effects through social networks

通过社会网络识别同伴效应

We provide new results regarding the identification of peer effects. We consider an extended version of the linear-in-means model where interactions are structured through a social network. We assume that correlated unobservables are either absent, or treated as network fixed effects. We provide easy-to-check necessary and sufficient conditions for identification. We show that endogenous and exogenous effects are generally identified under network interaction, although…

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Following Germany's Lead: Using International Monetary Linkages to Estimate the Effect of Monetary Policy on the Economy

追随德国:利用国际货币联系估计货币政策的经济效应

Forward-looking behavior on the part of the monetary authority makes it difficult to estimate the effect of monetary policy interventions on output. We present instrumental variables estimates of the impact of interest rates on quarterly real output for several European countries, using German interest rates as the instrument. These estimates confirm a strong forward-looking bias in least squares estimates that persists even conditional on standard controls for the history…

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Improved JIVE Estimators for Overidentified Linear Models with and without Heteroskedasticity

同方差与异方差过度识别线性模型的改进 JIVE 估计量

We introduce two simple new variants of the jackknife instrumental variables (JIVE) estimator for overidentified linear models and show that they are superior to the existing JIVE estimator, significantly improving on its small-sample-bias properties. We also compare our new estimators to existing Nagar (1959) type estimators. We show that, in models with heteroskedasticity, our estimators have superior properties to both the Nagar estimator and the related B2SLS estimator…

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Asymptotics in response-adaptive designs generated by a two-color, randomly reinforced urn

双色随机强化 urn 生成的反应自适应设计:渐近性质

This paper illustrates asymptotic properties for a response-adaptive design generated by a two-color, randomly reinforced urn model. The design considered is optimal in the sense that it assigns patients to the best treatment, with probability converging to one. An approach to show the joint asymptotic normality of the estimators of the mean responses to the treatments is provided in spite of the fact that allocation proportions converge to zero and one. Results on the rate…

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Estimating distributions of potential outcomes using local instrumental variables with an application to changes in college enrollment and wage inequality

用局部工具变量估计潜在结果分布:大学入学与工资不平等应用

This paper extends the method of local instrumental variables developed by Heckman and Vytlacil [Heckman, J., Vytlacil E., 2005. Structural equations, treatment, effects and econometric policy evaluation. Econometrica 73(3), 669-738] to the estimation of not only means, but also distributions of potential outcomes. The newly developed method is illustrated by applying it to changes in college enrollment and wage inequality using data from the National Longitudinal Survey of…

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Reform of Unemployment Compensation in Germany: A Nonparametric Bounds Analysis Using Register Data

德国失业补偿制度改革:基于行政登记数据的非参数界限分析

Economic theory suggests that an extension of the maximum length of entitlement for unemployment benefits increases the duration of unemployment. Empirical results for the reform of the unemployment compensation system in Germany during the 1980s are less clear. The analysis in this article is motivated by recent developments in econometrics for partial identification. We use extensive administrative data with the drawback that we can only observe lower and upper bounds for…

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Efficient Estimation of Average Treatment Effects with Mixed Categorical and Continuous Data

混合类别与连续协变量下平均处理效应的有效估计

In this article, we consider the nonparametric estimation of average treatment effects when there exist mixed categorical and continuous covariates. One distinguishing feature of the approach presented herein is the use of kernel smoothing for both the continuous and the discrete covariates. This approach, together with the cross-validation method. which we use for selecting the smoothing parameters, has the ability to automatically remove irrelevant covariates. We establish…

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Screening Experiments for Developing Dynamic Treatment Regimes

用于开发动态治疗方案的筛选实验

Dynamic treatment regimes are time-varying treatments that individualize sequences of treatments to the patient. The construction of dynamic treatment regimes is challenging because a patient will be eligible for some treatment components only if he has not responded (or has responded) to other treatment components. In addition there are usually a number of potentially useful treatment components and combinations thereof. In this article, we propose new methodology for…

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Likelihood-Based Analysis of Causal Effects of Job-Training Programs Using Principal Stratification

利用主分层对职业培训项目因果效应进行似然分析

Government-sponsored job-training programs must be subject to evaluation to assess whether their effectiveness justifies their cost to the public. The evaluation usually focuses on employment and total earnings, although the effect on wages is also of interest, because this effect reflects the increase in human capital due to the training program, whereas the effect on total earnings may be simply reflecting the increased likelihood of employment without any effect on wage…

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Robust Estimation of Mean Functions and Treatment Effects for Recurrent Events Under Event-Dependent Censoring and Termination: Application to Skeletal Complications in Cancer Metastatic to Bone

事件依赖删失与终止下复发事件均值函数及治疗效应的稳健估计

In clinical trials featuring recurrent clinical events, the definition and estimation of treatment effects involves a number of interesting issues, especially when loss to follow-up may be event-related and when terminal events such as death preclude the occurrence of further events. This paper discusses a clinical trial of breast cancer patients with bone metastases where the recurrent events are skeletal complications, and where patients may die during the trial. We argue…

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Estimating Response-Maximized Decision Rules With Applications to Breastfeeding

估计最大化结局的决策规则:母乳喂养应用

To estimate the sequence of actions that optimizes response in a longitudinal setting, it is important to study the actions as part of a set of decision rules rather than in a series of single-action comparisons. We take as our motivating example the estimation of the set of decision rules for the duration of breastfeeding with a view to maximizing infant growth. Breastfeeding has many well-recognized beneficial effects on health and development. However observational…

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Public insurance and private savings: who is affected and by how much?

公共保险与私人储蓄:谁受影响,影响多大?

Abstract This paper employs a recently developed instrumental quantile regression method to investigate the effect of Medicaid on household savings across different wealth groups. It finds that the disincentive effect of Medicaid on household savings is heavily concentrated in the middle net‐worth households. In contrast, the effects on the bottom and top net‐worth households are quite small and insignificant. These heterogeneous incentive effects are partly explained by…

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Dealing with limited overlap in estimation of average treatment effects

平均处理效应估计中的有限重叠问题

Estimation of average treatment effects under unconfounded or ignorable treatment assignment is often hampered by lack of overlap in the covariate distributions between treatment groups. This lack of overlap can lead to imprecise estimates, and can make commonly used estimators sensitive to the choice of specification. In such cases researchers have often used ad hoc methods for trimming the sample. We develop a systematic approach to addressing lack of overlap. We…

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Efficient nonparametric estimation of causal effects in randomized trials with noncompliance

存在不依从的随机试验中因果效应的高效非参数估计

Causal approaches based on the potential outcome framework provide a useful tool for addressing noncompliance problems in randomized trials. We propose a new estimator of causal treatment effects in randomized clinical trials with noncompliance. We use the empirical likelihood approach to construct a profile random sieve likelihood and take into account the mixture structure in outcome distributions, so that our estimator is robust to parametric distribution assumptions and…

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Non-Parametric Tests for Distributional Treatment Effect for Randomly Censored Responses

随机删失结果的分布处理效应非参数检验

Summary For a binary treatment ν = 0, 1 and the corresponding ‘potential response’Y0 for the control group (ν = 0) and Y1 for the treatment group (ν = 1), one definition of no treatment effect is that Y0 and Y1 follow the same distribution given a covariate vector X. Koul and Schick have provided a non-parametric test for no distributional effect when the realized response (1−ν)Y0+νY1 is fully observed and the distribution of X is the same across the two groups. This test is…

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Sequential Causal Models for the Evaluation of Labor Market Programs

劳动力市场项目评价中的序贯因果模型

This article reviews inverse selection probability weighting to estimate dynamic causal effects. A distribution theory based on sequential generalized method of moments estimation is proposed and the method is applied to a reevaluation of some parts of the Swiss active labor market policy to obtain new results and discuss several issues about the implementation of the estimation procedure.

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Random Recursive Partitioning: a matching method for the estimation of the average treatment effect

随机递归划分:估计平均处理效应的匹配方法

Abstract In this paper we introduce the Random Recursive Partitioning (RRP) matching method. RRP generates a proximity matrix which might be useful in econometric applications like average treatment effect estimation. RRP is a Monte Carlo method that randomly generates non‐empty recursive partitions of the data and evaluates the proximity between two observations as the empirical frequency they fall in a same cell of these random partitions over all Monte Carlo replications.…

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More on monotone instrumental variables

单调工具变量的进一步分析

Econometric analyses of treatment response often use instrumental variable (IV) assumptions to identify treatment effects. The traditional IV assumption holds that mean response is constant across the sub-populations of persons with different values of an observed covariate. Manski and Pepper (2000) introduced monotone instrumental variable assumptions, which replace equalities with weak inequalities. This paper presents further analysis of the monotone instrumental variable…

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Asymptotics for Statistical Treatment Rules

统计处理规则的渐近理论

This paper develops asymptotic optimality theory for statistical treatment rules in smooth parametric and semiparametric models. Manski (2000, 2002, 2004) and Dehejia (2005) have argued that the problem of choosing treatments to maximize social welfare is distinct from the point estimation and hypothesis testing problems usually considered in the treatment effects literature, and advocate formal analysis of decision procedures that map empirical data into treatment choices.…

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