How do medical researchers make causal inferences?

Bradford Hill (1965) highlighted nine aspects of the complex evidential situation a medical researcher faces when determining whether a causal relation exists between a disease and various conditions associated with it. These aspects are widely cited in the literature on epidemiological inference as justifying an inference to a causal claim, but the epistemological basis of the Hill aspects is not understood. We offer an explanatory coherentist interpretation, explicated by Thagard's ECHO model of explanatory coherence. The ECHO model captures the complexity of epidemiological inference and provides a tractable model for inferring disease causation. We apply this model to three cases: the inference of a causal connection between the Zika virus and birth defects, the classic inference that smoking causes cancer, and John Snow’s inference about the cause of cholera

Antireductionist Interventionism

Kim’s causal exclusion argument purports to demonstrate that the non-reductive physicalist must treat mental properties (and macro-level properties in general) as causally inert. A number of authors have attempted to resist Kim’s conclusion by utilizing the conceptual resources of Woodward’s (2005) interventionist conception of causation. The viability of these responses has been challenged by Gebharter (2017a), who argues that the causal exclusion argument is vindicated by the theory of causal Bayesian networks (CBNs). Since the interventionist conception of causation relies crucially on CBNs for its foundations, Gebharter’s argument appears to cast significant doubt on interventionism’s antireductionist credentials. In the present article, we both (1) demonstrate that Gebharter’s CBN-theoretic formulation of the exclusion argument relies on some unmotivated and philosophically significant assumptions (especially regarding the relationship between CBNs and the metaphysics of causal relevance), and (2) use Bayesian networks to develop a general theory of causal inference for multi-level systems that can serve as the foundation for an antireductionist interventionist account of causation

Causal Pluralism in Philosophy: Empirical Challenges and Alternative Proposals

An increasing number of arguments for causal pluralism invoke empirical psychological data. Different aspects of causal cognition-specifically, causal perception and causal inference-are thought to involve distinct cognitive processes and representations, and they thereby distinctively support transference and dependency theories of causation, respectively. We argue that this dualistic picture of causal concepts arises from methodological differences, rather than from an actual plurality of concepts. Hence, philosophical causal pluralism is not particularly supported by the empirical data. Serious engagement with cognitive science reveals that the connection between psychological concepts of causation and philosophical notions is substantially more complicated than is traditionally presumed

Simpson's paradox: A logically benign, empirically treacherous hydra

This article examines Simpson's paradox as applied to the theory of probabilites and percentages. The author discusses possible flaws in the paradox and compares it to the Sure Thing Principle, statistical inference, causal inference and probabilistic analyses of causation

Causal Pluralism in Philosophy: Empirical Challenges and Alternative Proposals

An increasing number of arguments for causal pluralism invoke empirical psychological data. Different aspects of causal cognition-specifically, causal perception and causal inference-are thought to involve distinct cognitive processes and representations, and they thereby distinctively support transference and dependency theories of causation, respectively. We argue that this dualistic picture of causal concepts arises from methodological differences, rather than from an actual plurality of concepts. Hence, philosophical causal pluralism is not particularly supported by the empirical data. Serious engagement with cognitive science reveals that the connection between psychological concepts of causation and philosophical notions is substantially more complicated than is traditionally presumed

Visual Causality: Investigating Graph Layouts for Understanding Causal Processes

Causal diagrams provide a graphical formalism indicating how statistical models can be used to study causal processes. Despite the extensive research on the efficacy of aesthetic graphic layouts, the causal inference domain has not benefited from the results of this research. In this paper, we investigate the performance of graph visualisations for supporting users’ understanding of causal graphs. Two studies were conducted to compare graph visualisations for understanding causation and identifying confounding variables in a causal graph. The first study results suggest that while adjacency matrix layouts are better for understanding direct causation, node-link diagrams are better for understanding mediated causation along causal paths. The second study revealed that node-link layouts, and in particular layouts created by a radial algorithm, are more effective for identifying confounder and collider variables

Are Reasons Causally Relevant for Action? Dharmakīrti and the Embodied Cognition Paradigm

How do mental states come to be about something other than their own operations, and thus to serve as ground for effective action? This papers argues that causation in the mental domain should be understood to function on principles of intelligibility (that is, on principles which make it perfectly intelligible for intentions to have a causal role in initiating behavior) rather than on principles of mechanism (that is, on principles which explain how causation works in the physical domain). The paper considers Dharmakīrti’s kāryānumāna argument (that is, the argument that an inference is sound only when one infers from the effect to the cause and not vice versa), and proposes a naturalized account of reasons. On this account, careful scrutiny of the effect can provide a basis for ascertaining the unique causal totality that is its source, but only for reasoning that is context‐specific

Mechanisms (Oxford)

Mechanism is undoubtedly a causal concept, in the sense that ordinary definitions and philosophical analyses explicate the concept in terms of other causal concepts such as production and interaction. Given this fact, many philosophers have supposed that analyses of the concept of mechanism, while they might appeal to philosophical theories about the nature of causation, could do little to inform such theories. On the other hand, methods of causal inference and explanation appeal to mechanisms. Discovering a mechanism is the gold standard for establishing and explaining causal connections. This fact suggests that it might be possible to provide an analysis of causation that appeals to mechanisms

Antireductionist Interventionism

Kim's causal exclusion argument purports to demonstrate that the non-reductive physicalist must treat mental properties (and macro-level properties in general) as causally inert. A number of authors have attempted to resist Kim's conclusion by utilizing the conceptual resources of Woodward's (2005) interventionist conception of causation. The viability of these responses has been challenged by Gebharter (2017a), who argues that the causal exclusion argument is vindicated by the theory of causal Bayesian networks (CBNs). Since the interventionist conception of causation relies crucially on CBNs for its foundations, Gebharter's argument appears to cast significant doubt on interventionism's antireductionist credentials. In the present article, we both (1) demonstrate that Gebharter's CBN-theoretic formulation of the exclusion argument relies on some unmotivated and philosophically significant assumptions (especially regarding the relationship between CBNs and the metaphysics of causal relevance), and (2) use Bayesian networks to develop a general theory of causal inference for multi-level systems that can serve as the foundation for an antireductionist interventionist account of causation

Causal Inference in Healthcare: Approaches to Causal Modeling and Reasoning through Graphical Causal Models

In the era of big data, researchers have access to large healthcare datasets collected over a long period. These datasets hold valuable information, frequently investigated using traditional Machine Learning algorithms or Neural Networks. These algorithms perform great in finding patterns out of datasets (as a predictive machine); however, the models lack extensive interpretability to be used in the healthcare sector (as an explainable machine). Without exploring underlying causal relationships, the algorithms fail to explain their reasoning. Causal Inference, a relatively newer branch of Artificial Intelligence, deals with interpretability and portrays causal relationships in data through graphical models. It explores the issue of causality and works towards an explainability of underlying causal models deeply buried in data. For this dissertation work, the research goal is to use Causal Inference to build an applied framework that lets researchers leverage observational datasets in understanding causal relationships between features. To achieve that, we focus on specific objectives such as (a) the addition of background knowledge to causal structure learning algorithms, (b) the proposal of new causal inference methodologies, (c) generation of theories connecting causality to standard statistical analyses (e.g., Odds Ratio, Survival Analysis), and (d) application of proposed approaches in real-world healthcare problems. This dissertation encapsulates the tasks mentioned above, through various new methodologies and experiments under the rubric of Structural Theory of Causation. We discuss the common research theme in causal inference, historical development, the structural theory of causation, and underlying assumptions. Finally, we explore the impact of these proposed methodologies in real-world treatment controversy of Delirium patients, by examining the efficacy of antipsychotic drugs prescribed in treating Delirium in the ICU, from a curated observational healthcare dataset