Accommodating stochastic departures from percentile invariance in causal models

Summary.  Consider a clinical trial in which participants are randomized to a single-dose treatment or a placebo control and assume that the adherence level is accurately recorded. If the treatment is effective, then good adherers in the treatment group should do better than poor ad- herers because they received more drug; the treatment group data follow a dose–response curve. But, good adherers to the placebo often do better than poor adherers, so the observed adherence–response in the treatment group cannot be completely attributed to the treatment. Efron and Feldman proposed an adjustment to the observed adherence–response in the treatment group by using the adherence–response in the control group. It relies on a percentile invariance assumption under which each participant's adherence percentile within their assigned treatment group does not depend on the assigned group (active drug or placebo). The Efron and Feldman approach is valid under percentile invariance, but not necessarily under departures from it. We propose an analysis based on a generalization of percentile invariance that allows adherence percentiles to be stochastically permuted across treatment groups, using a broad class of stochastic permutation models. We show that approximate maximum likelihood estimates of the underlying dose–response curve perform well when the stochastic permutation process is correctly specified and are quite robust to model misspecification.     

The effects of job displacement on job quality: Findings from the Wisconsin Longitudinal Study

This study examines the extent to which job displacement divides the career experiences for a cohort of workers. Previous studies of job displacement find nontrivial economic losses for displaced workers, but the effects of displacement on “non-economic” properties of jobs have been largely overlooked. Results using the Wisconsin Longitudinal Study indicate that workers who were displaced have lower levels of occupational status, job authority, and employer-offered pension and health insurance than they would have had had they not been displaced. Difference-in-differences estimates, which control for temporally-invariant unobserved heterogeneity, are generally larger than cross-sectional estimates; still, there is a close correspondence of estimates across a range of methodological approaches attesting to the robustness of the estimates in the face of various technical assumptions and model specifications. Effects of displacement on job quality also exhibit conditioning by gender, education, occupation, and industry: while less educated, blue collar and manufacturing workers experience significant losses for employer-offered benefits, more educated, upper white collar and non-manufacturing workers experience significant losses for occupational status, job autonomy, and job authority.     

Causal inference for recurrent events data with all-or-none compliance

ABSTRACT

In this article, causal inference in randomized studies with recurrent events data and all-or-none compliance is considered. We use the counting process to analyze the recurrent events data and propose a causal proportional intensity model. The maximum likelihood approach is adopted to estimate the parameters of the proposed causal model. To overcome the computational difficulties created by the mixture structure of the problem, we develop an expectation-maximization (EM) algorithm. The resulting estimators are shown to be consistent and asymptotically normal. We further estimate the complier average causal effect (CACE), which is defined as the difference of the average numbers of recurrence between treatment and control groups within the complier class. The corresponding inferential procedures are established. Some simulation studies are conducted to assess the finite sample performance of the proposed approach.     

论强纲领中的因果性模型问题

布鲁尔强纲领中所反对的目的论模型来源于巴恩斯提出的目的论模式，他们都拒绝将科学的发展看作真理进化过程，认为作为一种信念的科学知识应当纳入知识社会学的研究领域。布鲁尔进一步提出了与目的论模型相对立的因果性模型，并构建出科学知识社会学应当遵守的四条“强纲领”，力图促使知识社会学成为一门普遍性的学科。布鲁尔声称强纲领的因果性模型与目的论模型是两种相互排斥的形而上学立场，但实际上因果性模型与目的论模型并不完全排斥，甚至还有所交融；同时，他关于因果性模型的论证与辩护也存在诸多问题，是一种不彻底的“强纲领”。     

《天龙八部》中的因果之说

《天龙八部》是金庸作品中和佛教关系最紧密的一部小说。金庸通过三位主角引出了芸芸众生相以及纷繁复杂的故事发展,而这一切到最后,全部都是有因可循的。通过分析主角及配角的经历和结局,来揭示《天龙八部》中所体现的因果之说。     

Causal effects of foster care: An instrumental-variables approach

This paper describes the use of instrumental-variables (IV) to estimate causal effects of foster care on long- and short-term outcomes. This estimation strategy provides a tool to evaluate what are known as “natural experiments”: settings that mimic randomization usually associated with a controlled trial. The proposed natural experiment involves the effective randomization of investigators to child-protection cases. The results suggest that foster care placement increases like likelihood of delinquency and emergency healthcare episodes. Care must be taken when interpreting IV estimates. The results apply to cases that are part of the natural experiment—“marginal cases” where the investigators may disagree about the placement recommendation.     

The Causal Impact of Resistance: Mediating between Resistance and Internal Conversation about Resistance

Current literature on resistance focuses on the elements of action and opposition as its main components. However, when we use the term resistance we are not necessarily referring exclusively to the active expression of opposition, but could also be referring to discussions about such events or to stimuli that may cause these acts. Thus resistance, for the purposes of this study, is perceived in terms of action, external conversation and stimuli, and it is argued that these external characteristics may be further processed through deliberation and internal conversations about resistance. An exploratory empirical study revealed inner aspects of resistance, and examined whether internal conversations about resistance could actually be experienced by agents. This article further supports the argument that, as individuals produce internal conversations about resistance, they may end by following one of the suggested options: they may keep their internal conversations unspoken, or produce a course of action related to resistance (and identified as such), or they may produce external conversations about resistance, or they may end by producing resistance that is not recognisable (to others) as such. In all these cases, internal conversations about resistance are involved and it is therefore argued that the causal impact of resistance may derive from agential processes and powers as well as from action, stimuli or external conversations related to resistance.     

Matching Using Sufficient Dimension Reduction for Causal Inference

ABSTRACT

To estimate causal treatment effects, we propose a new matching approach based on the reduced covariates obtained from sufficient dimension reduction. Compared with the original covariates and the propensity score, which are commonly used for matching in the literature, the reduced covariates are nonparametrically estimable and are effective in imputing the missing potential outcomes, under a mild assumption on the low-dimensional structure of the data. Under the ignorability assumption, the consistency of the proposed approach requires a weaker common support condition. In addition, researchers are allowed to employ different reduced covariates to find matched subjects for different treatment groups. We develop relevant asymptotic results and conduct simulation studies as well as real data analysis to illustrate the usefulness of the proposed approach.     

An analytic method for randomized trials with informative censoring: Part II

Consider a randomized trial in which time to the occurrence of a particular disease, say pneumocystic pneumonia in an AIDS trial or breast cancer in a mammographic screening trial, is the failure time of primary interest. Suppose that time to disease is subject to informative censoring by the minimum of time to death, loss to and end of follow-up. In such a trial, the potential censoring time is observed for all study subjects, including failures. In the presence of informative censoring, it is not possible to consistently estimate the effect of treatment on time to disease without imposing additional non-identifiable assumptions. Robins (1995) specified two non-identifiable assumptions that allow one to test for and estimate an effect of treatment on time to disease in the presence of informative censoring. The goal of this paper is to provide a class of consistent and reasonably efficient semiparametric tests and estimators for the treatment effect under these assumptions. The tests in our class, like standard weighted-log-rank tests, are asymptotically distribution-free -level tests under the null hypothesis of no causal effect of treatment on time to disease whenever the censoring and failure distributions are conditionally independent given treatment arm. However, our tests remain asymptotically distribution-free -level tests in the presence of informative censoring provided either of our assumptions are true. In contrast, a weighted log-rank test will be an -level test in the presence of informative censoring only if (1) one of our two non-identifiable assumptions hold, and (2) the distribution of time to censoring is the same in the two treatment arms. We also study the estimation, in the presence of informative censoring, of the effect of treatment on the evolution over time of the mean of repeated measures outcome such as CD4 count.     

Linking Perceived Quality and Customer Satisfaction to Store Traffic and Revenue Growth*

Effects of perceived merchandise and service quality, relative to competition, on retail store performance are investigated using store traffic and revenue growth as outcome variables. A model is proposed and tested using aggregate customer data and store performance outcomes from a group of stores owned by a national retail organization. Results suggest that both service and merchandise quality exert significant influence on store performance, measured by sales growth and customer growth, and their impact is mediated by customer satisfaction. Implications of the results and future research directions are discussed.