Causal Data Integration
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Causal inference is fundamental to empirical scientific discoveries in natural and social sciences; however, in the process of conducting causal inference, data management problems can lead to false discoveries. Two such problems are (i) not having all attributes required for analysis, and (ii) misidentifying which attributes are to be included in the analysis. Analysts often only have access to partial data, and they critically rely on (often unavailable or incomplete) domain knowledge to identify attributes to include for analysis, which is often given in the form of a causal DAG. We argue that data management techniques can surmount both of these challenges. In this work, we introduce the Causal Data Integration (CDI) problem, in which unobserved attributes are mined from external sources and a corresponding causal DAG is automatically built. We identify key challenges and research opportunities in designing a CDI system, and present a system architecture for solving the CDI problem. Our preliminary experimental results demonstrate that solving CDI is achievable and pave the way for future research.Keywords:
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Causality is central to our understanding of the world and central to scientific explanation. In recent years two approaches to causality have come to prominence and have had a major impact on the social sciences: these are the counterfactual or potential outcomes model of causality and the approach that understands causality in terms of a causal structure represented by a graph. I present both of these and explain how they can be used to identify causal relationships in situations when we do not have access to experimental data. I discuss the principles underlying the most widely used strategies for estimating causal effects in these situations. Finally, I discuss questions of external validity, and, in particular, the conditions under which sociologists' causal estimates can be of more than historical interest.
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Causal inference is a study of causal relationships between events and the statistical study of inferring these relationships through interventions and other statistical techniques. Causal reasoning is any line of work toward determining causal relationships, including causal inference. This paper explores the relationship between causal reasoning and various fields of software engineering. This paper aims to uncover which software engineering fields are currently benefiting from the study of causal inference and causal reasoning, as well as which aspects of various problems are best addressed using this methodology. With this information, this paper also aims to find future subjects and fields that would benefit from this form of reasoning and to provide that information to future researchers. This paper follows a systematic literature review, including; the formulation of a search query, inclusion and exclusion criteria of the search results, clarifying questions answered by the found literature, and synthesizing the results from the literature review. Through close examination of the 45 found papers relevant to the research questions, it was revealed that the majority of causal reasoning as related to software engineering is related to testing through root cause localization. Furthermore, most causal reasoning is done informally through an exploratory process of forming a Causality Graph as opposed to strict statistical analysis or introduction of interventions. Finally, causal reasoning is also used as a justification for many tools intended to make the software more human-readable by providing additional causal information to logging processes or modeling languages.
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Qualitative models of dynamical systems fall into non-causal or causal approaches. The non-causal approach is widely used in part because traditional physics describes phenomena by means of symmetric functional relations. It supports the idea that causality can be ignored or inferred from the model itself. Nevertheless, when people explain how things work, they use causal relations. Representing causality explicitly makes it possible to take advantage of exogenous knowledge necessary for understanding the phenomena and supporting self-explanatory simulation. The basic concepts used in both approaches, in addition to the representation formalisms and algorithms, are discussed in the light of recent works performed by teams affiliated to the MQ&D research group in France.
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Statisticians and social and computer scientists tend to approach causality and causal inference with particular theories of causality in mind, and defend tools that are supposed to support causal claims from the point of view of that theory. This entry explains why theoretical and methodological pluralism with respect to causality can benefit causal inference. To this aim, we first discuss various understandings of the concept of causality, and of mechanisms, and emphasize that none of them can be considered as intrinsically superior to another. We then discuss typical design‐ and model‐based identification strategies of causal effects from within the potential outcome approach, and point to the crucial role of untestable assumptions for defending causal claims within experimental and observational methods. Finally, we explain how computational tools like agent‐based modeling can aid causal inference, and argue that persuasive causal claims in fact require data and arguments produced by methods that are based on different assumptions and that incorporate different views of causality and mechanisms.
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Based on the structural causal model, this study derived a causal graph that shows the causal relationship between the factors predicting the teaching competency of lower secondary school teachers in South Korea, the UK(England), and Finland. Also, it compared and analyzed the causal path to each country’s teaching competency. To this end, the data of lower secondary school teachers and principals, who participated in TALIS 2018, in Korea, the UK(England), and Finland were analyzed. First, the top 20 factors that predict teaching competency by each country were extracted by applying the mixed-effect random forest technique in consideration of the multi-layer structure of the data. Then, the causal graphs were derived by applying the causal discovery algorithm based on a structural causal model with the extracted predictors. As a result, there were common factors and discrimination factors in the top 20 predictors extracted from each national data, and the causal paths to teaching competency were compared and analyzed in the context of each country based on the causal graph by country. In addition, in the field of education, the possibility of using causal inference based on structural causal models was discussed, and the limitations and implications of this study were presented.
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Abstract We used a new method to assess how people can infer unobserved causal structure from patterns of observed events. Participants were taught to draw causal graphs, and then shown a pattern of associations and interventions on a novel causal system. Given minimal training and no feedback, participants in Experiment 1 used causal graph notation to spontaneously draw structures containing one observed cause, one unobserved common cause, and two unobserved independent causes, depending on the pattern of associations and interventions they saw. We replicated these findings with less‐informative training (Experiments 2 and 3) and a new apparatus (Experiment 3) to show that the pattern of data leads to hidden causal inferences across a range of prior constraints on causal knowledge.
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Abstract : Causal claims are unavoidable in military affairs. However, causal claims also are insufficient when attempting to understand and intervene in complex environments. Hence, notions of conventional causality must be supplemented with an understanding of emergent causality. This paper examines three competing claims about the decline in violence in Iraq from 2007 to 2008 from two perspectives: Craig Parsons's logics of causal explanation and William Connolly's concept of emergent causality. I find that an understanding of both types of causality is necessary for a full appreciation of what happened in Iraq. I argue that the military professional requires a nuanced understanding of conventional causality since such claims are integral to understanding and interventions. However, the military professional also requires also a nuanced understanding of emergent causality and an accompanying philosophy for how to intervene in the world.
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Although a number of investigators have attempted to identify empirically a process of political development, substantial controversy still surrounds a determination of the causal factors involved. It is my contention that this state of affairs is the result of inadequacies inherent in traditional techniques of causal modeling, and aggravated when multicollinear variables are involved. To resolve this problem I first review a technique capable of reducing the confounding effects of multicollinearity. I then illustrate use of this technique, as well as a strategy for inferring causal relationships, by means of a reanalysis of published data used to construct models of political development. The strategy for causal inference utilized herein is derived from knowledge of the effects of model specification errors. On the basis of these findings a new causal model of political development, which is both theoretically and empirically consistent, is presented.
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Causal inference is a study of causal relationships between events and the statistical study of inferring these relationships through interventions and other statistical techniques. Causal reasoning is any line of work toward determining causal relationships, including causal inference. This paper explores the relationship between causal reasoning and various fields of software engineering. This paper aims to uncover which software engineering fields are currently benefiting from the study of causal inference and causal reasoning, as well as which aspects of various problems are best addressed using this methodology. With this information, this paper also aims to find future subjects and fields that would benefit from this form of reasoning and to provide that information to future researchers. This paper follows a systematic literature review, including; the formulation of a search query, inclusion and exclusion criteria of the search results, clarifying questions answered by the found literature, and synthesizing the results from the literature review. Through close examination of the 45 found papers relevant to the research questions, it was revealed that the majority of causal reasoning as related to software engineering is related to testing through root cause localization. Furthermore, most causal reasoning is done informally through an exploratory process of forming a Causality Graph as opposed to strict statistical analysis or introduction of interventions. Finally, causal reasoning is also used as a justification for many tools intended to make the software more human-readable by providing additional causal information to logging processes or modeling languages.
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