Alternative causal inference methods in population health research: Evaluating tradeoffs and triangulating evidence.

Population health researchers from different fields often address similar substantive questions but rely on different study designs, reflecting their home disciplines. This is especially true in studies involving causal inference, for which semantic and substantive differences inhibit interdisciplinary dialogue and collaboration. In this paper, we group nonrandomized study designs into two categories: those that use confounder-control (such as regression adjustment or propensity score matching) and those that rely on an instrument (such as instrumental variables, regression discontinuity, or differences-in-differences approaches). Using the Shadish, Cook, and Campbell framework for evaluating threats to validity, we contrast the assumptions, strengths, and limitations of these two approaches and illustrate differences with examples from the literature on education and health. Across disciplines, all methods to test a hypothesized causal relationship involve unverifiable assumptions, and rarely is there clear justification for exclusive reliance on one method. Each method entails trade-offs between statistical power, internal validity, measurement quality, and generalizability. The choice between confounder-control and instrument-based methods should be guided by these tradeoffs and consideration of the most important limitations of previous work in the area. Our goals are to foster common understanding of the methods available for causal inference in population health research and the tradeoffs between them; to encourage researchers to objectively evaluate what can be learned from methods outside one's home discipline; and to facilitate the selection of methods that best answer the investigator's scientific questions

Designing Research With Qualitative Comparative Analysis (QCA): Approaches, Challenges, and Tools

Recent years have witnessed a host of innovations for conducting research with qualitative comparative analysis (QCA). Concurrently, important issues surrounding its uses have been highlighted. In this article, we seek to help users design QCA studies. We argue that establishing inference with QCA involves three intertwined design components: first, clarifying the question of external validity; second, ensuring internal validity; and third, explicitly adopting a specific mode of reasoning. We identify several emerging approaches to QCA rather than just one. Some approaches emphasize case knowledge, while others are condition oriented. Approaches emphasize either substantively interpretable or redundancy-free explanations, and some designs apply an inductive/explorative mode of reasoning, while others integrate deductive elements. Based on extant literature, we discuss issues surrounding inference with QCA and the tools available under different approaches to address these issues. We specify trade-offs and the importance of doing justice to the nature and goals of QCA in a specific research context

Continuous Improvement Through Knowledge-Guided Analysis in Experience Feedback

Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector

Economic Analysis and Statistical Disclosure Limitation

This paper explores the consequences for economic research of methods used by data publishers to protect the privacy of their respondents. We review the concept of statistical disclosure limitation for an audience of economists who may be unfamiliar with these methods. We characterize what it means for statistical disclosure limitation to be ignorable. When it is not ignorable, we consider the effects of statistical disclosure limitation for a variety of research designs common in applied economic research. Because statistical agencies do not always report the methods they use to protect confidentiality, we also characterize settings in which statistical disclosure limitation methods are discoverable; that is, they can be learned from the released data. We conclude with advice for researchers, journal editors, and statistical agencies

Back to Basics—Research Design for the Operational Level of War

The military-research criteria gap has been a challenge for researchers and planners. By examining the nature of war fighting and exploring how we could better assess, select, and evaluate research methods, we will create a more informed research process, ultimately leading to improved practices and more-credible outcomes

Connectionist Inference Models

The performance of symbolic inference tasks has long been a challenge to connectionists. In this paper, we present an extended survey of this area. Existing connectionist inference systems are reviewed, with particular reference to how they perform variable binding and rule-based reasoning, and whether they involve distributed or localist representations. The benefits and disadvantages of different representations and systems are outlined, and conclusions drawn regarding the capabilities of connectionist inference systems when compared with symbolic inference systems or when used for cognitive modeling