Inverse Probability Weighting with Missing Predictors of Treatment Assignment or Missingness

Inverse probability weighting (IPW) can deal with confounding in non randomized studies. The inverse weights are probabilities of treatment assignment (propensity scores), estimated by regressing assignment on predictors. Problems arise if predictors can be missing. Solutions previously proposed include assuming assignment depends only on observed predictors and multiple imputation (MI) of missing predictors. For the MI approach, it was recommended that missingness indicators be used with the other predictors. We determine when the two MI approaches, (with/without missingness indicators) yield consistent estimators and compare their efficiencies.We find that, although including indicators can reduce bias when predictors are missing not at random, it can induce bias when they are missing at random. We propose a consistent variance estimator and investigate performance of the simpler Rubin’s Rules variance estimator. In simulations we find both estimators perform well. IPW is also used to correct bias when an analysis model is fitted to incomplete data by restricting to complete cases. Here, weights are inverse probabilities of being a complete case. We explain how the same MI methods can be used in this situation to deal with missing predictors in the weight model, and illustrate this approach using data from the National Child Development Survey.     

Did the Military Interventions in the Mexican Drug War Increase Violence?

We analyze publicly available data to estimate the causal effects of military interventions on the homicide rates in certain problematic regions in Mexico. We use the Rubin causal model to compare the post-intervention homicide rate in each intervened region to the hypothetical homicide rate for that same year had the military intervention not taken place. Because the effect of a military intervention is not confined to the municipality subject to the intervention, a nonstandard definition of units is necessary to estimate the causal effect of the intervention under the standard no-interference assumption of stable-unit treatment value assumption (SUTVA). Donor pools are created for each missing potential outcome under no intervention, thereby allowing for the estimation of unit-level causal effects. A multiple imputation approach accounts for uncertainty about the missing potential outcomes.     

Child welfare and the challenge of causal inference

Causal inference refers to the assessment of cause and effect relationships in observational data—i.e., in situations where random assignment is impossible or impractical. Choices involving children in the child welfare system evoke core elements of causal inference—manipulation and the counterfactual. How would a child's circumstances differ if we changed her environment? This article begins with one mathematical approach to framing causal inference, the potential outcomes framework. This methodology is a cornerstone of newer approaches to causal inference. This framework makes clear the identification problem inherent in causal inference and highlights a key assumption often used to identify the model (ignorability or no unobserved confounding). The article then outlines the various approaches to causal inference and organizes them around whether they assume ignorability as well as other key features of each approach. The article then provides guidelines for producing good causal inference. These guidelines emerge from a review of methodological literature as broad ranging as epidemiology, statistics, economics, and policy analysis. These steps will be illustrated using an example from child welfare. The article concludes with suggestions for how the field could apply these newer methods.     

Optimal dynamic treatment regimes

Summary. A dynamic treatment regime is a list of decision rules, one per time interval, for how the level of treatment will be tailored through time to an individual's changing status. The goal of this paper is to use experimental or observational data to estimate decision regimes that result in a maximal mean response. To explicate our objective and to state the assumptions, we use the potential outcomes model. The method proposed makes smooth parametric assumptions only on quantities that are directly relevant to the goal of estimating the optimal rules. We illustrate the methodology proposed via a small simulation.     

基于协变量平衡加权的平均处理效应的稳健有效估计

倾向性得分是估计平均处理效应的重要工具。但在观察性研究中,通常会由于协变量在处理组与对照组分布的不平衡性而导致极端倾向性得分的出现,即存在十分接近于0或1的倾向性得分,这使得因果推断的强可忽略假设接近于违背,进而导致平均处理效应的估计出现较大的偏差与方差。Li等(2018a)提出了协变量平衡加权法,在无混杂性假设下通过实现协变量分布的加权平衡,解决了极端倾向性得分带来的影响。本文在此基础上,提出了基于协变量平衡加权法的稳健且有效的估计方法,并通过引入超级学习算法提升了模型在实证应用中的稳健性;更进一步,将前一方法推广至理论上不依赖于结果回归模型和倾向性得分模型假设的基于协变量平衡加权的稳健有效估计。蒙特卡洛模拟表明,本文提出的两种方法在结果回归模型和倾向性得分模型均存在误设时仍具有极小的偏差和方差。实证部分将两种方法应用于右心导管插入术数据,发现右心导管插入术大约会增加患者6. 3%死亡率。     

Analysis of the Threshold Values of Semantic Structure Analysis in Identifying Causal Relationships

This study uses a semantic structure analysis (SSA) method to construct the causal relationships among the criteria from survey data. The literatures provide a predetermined threshold value when the SSA is applied without explanation, but we use a Monte Carlo simulation based on the raw data to determine the threshold values with the significant levels of 0.05 and 0.10 for constructing the causal relationships. The results show that the causal relationships among the criteria using the suggested threshold value are too complicated, while the causal relationships by the simulated threshold values are relatively easy to be understood and used practically.     

Nurses' representations of the perceived causes of work-related stress: a network drawing approach

This paper reports a study, involving a network drawing approach, that examined how nurses perceive the interrelationship between causes of workplace stress. Network analysis originated in sociology as a method of examining the relationship between people, objects or events. It has recently been adapted to examine participants' perceptions of the relationships between causes of a phenomenon, either by asking participants to complete a grid rating the strength of all the possible links between causes or by getting them to draw a diagram of the links that they think are important. The network drawing technique, in which participants are asked to draw a diagram indicating perceived causal links between nominated causes of stress and also to indicate the strength of these links, was employed in this study. The causes of stress were taken from a previous study in which nurses kept a diary for one week detailing stressful events and their causes. There were 48 participants in the present study and the main results confirmed the importance of staffing levels and inadequate support as perceived direct causes of stress. The study also revealed the importance of indirect links between staffing levels and other causes of stress. The networks illustrate how direct and mediating causes of stress are connected and lie largely outside nurses' control. The results are discussed in relation to other recent work on the causes and experience of stress by nursing staff.     

Estimating a Marginal Causal Odds Ratio Subject to Confounding

Odds ratios are frequently used to describe the relationship between a binary treatment or exposure and a binary outcome. An odds ratio can be interpreted as a causal effect or a measure of association, depending on whether it involves potential outcomes or the actual outcome. An odds ratio can also be characterized as marginal versus conditional, depending on whether it involves conditioning on covariates. This article proposes a method for estimating a marginal causal odds ratio subject to confounding. The proposed method is based on a logistic regression model relating the outcome to the treatment indicator and potential confounders. Simulation results show that the proposed method performs reasonably well in moderate-sized samples and may even offer an efficiency gain over the direct method based on the sample odds ratio in the absence of confounding. The method is illustrated with a real example concerning coronary heart disease.     

Correcting for non-compliance in randomized trials using structural nested mean models

In a randomized trial designed to study the effect of a treatment of interest on the evolution of the mean of a time-dependent outcome variable, subjects are assigned to a treatment regime, or, equivalently, a treatment protocol. Unfortunately, subjects often fail to comply with their assigned regime. From a public health point of view, the causal parameter of interest will often be a function of the treatment differences that would have been observed hadcontrary to fact, all subjects remained on protocol. This paper considers the identification and estimation of these treatment differences based on a new class of structural models, the multivariate structural nested mean models, when reliable estimates of each subject's actual treatment are available. Estimates of “actual treatment” might, for example, be obtained by measuring the amount of “active drug” in the subject's blood or urine at each follow-up visit or by pill counting techniques. In addition, we discuss a natural extension of our methods to observational studies.     

企业经营原因不明中的企业家能力

产业组织理论将企业经营绩效的决定归因于外部因素,而企业的资源、能力观则试图从企业内部来解释经营绩效,其中,原因不明是一个重要的难以模仿的资源,成为企业获得持续竞争优势的重要基础。本文对原因不明的内涵、特征进行了探讨,指出对企业家能力的忽视是导致原因不明的重要原因,抓住了企业家因素,原因不明似乎可以得到解释了。