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Definitions of Causation: Analysis of New Areas

Strengths and Weaknesses of Definitions of Causation

According to Parascandola and Weed (2001), there are several definitions of causations: “production, necessary causes, sufficient component causes, probabilistic causes and counterfactuals” (p. 906). However, while evaluating the strengths and weaknesses of diseases, it has been found that not all the definitions are self-efficient because of absence of necessary empirical evidence and theoretical support. To begin with, the production cause creates bias concerning what the actual production process involves since it is not clear how a cause produces effect. A necessary cause implies a one-to-one correspondence and, therefore, there should be only a single cause and a single effect. Sufficient-component cause is an addition to a necessary cause because it involves a complex of elements that shape sufficient explanation of a causation, but are not sufficient if they are represented separately.

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Therefore, sufficient-component cause is challenged by generating models that incorporate the dynamics of causal relations. Probabilistic causation fills in the gaps of the category defining cause as neither sufficient nor probabilistic. In this respect, the probabilistic causation reveals the chance of an effect to occur. At the same time, this definition fails to explain the sufficient-component conditions. Finally, counterfactuals shape conditions under which one outcome is ensured in case of a certain condition and an alternative outcome can be presented in case of an another condition. The definitions cannot be presented as self-efficient because; instead, it can complement sufficient-component cause, necessary and probabilistic causes. According to Parascandola and Weed (2001), the greatest definition is formed by the combination of the counterfactual condition and probabilistic definition because it meets the requirements of both probabilistic and deterministic models of causation.

Analyzing New Domains and Definitions of Causation

Parascandola and Weed (2001) state, “there is no reason to assert that causes at one level, such as molecules, are any more real or significant than causes at another level, such as social factors”. The hierarchical structure of causes is also represented by Maclure and Schneeweiss (2001). In particular, the researchers have presented the causation model in the form of episcope which allows to define the direction of causation and highlight all possible deviations and biases of causal inferences (Maclure & Schneeweiss, 2001). At this point, the models of causation are created in accordance with specific domains and criteria that will be based on a specific example. Based on the example represented in the article, another qualitative study will be analyzed in accordance with the highlighted criteria, starting with agent of interest and ending with knowledge of meta-analysis.

In the article, Legare (2011) focuses on shared decision making that leads to informed content and person-oriented care. Therefore, the decision making process will be the agent of interest causing further outcomes. Before shared-decision making process is implemented, knowledge-to-action processes occur that increase the cooperation between caregivers and their patients, which is the background factor. The connection between effective communication and shared decision-making is evident and, therefore, it can be presented as a non-random causation. To define the tools and strategies for enhancing decision-sharing, communication and cooperation is neither sufficient nor necessary causes because there are many other factors that increase the probability of effective client-centered care, which defines the domain of diagnosing and encoding.

The next stage implies loss of information and, as a result, the identified causation can be built on this bias. To define this, it is necessary to conduct an empirical study, which represented in the article in the form of interviews. To support the empirical studies, Legare et al. (2011) analyze the obtained data from the perspective of theory of planned behavior to define whether the hypothesis is true or not. Defining all possible losses, as represented in the harvesting stage, is necessary to understand the major causes of losses and possible deviations from the presented model of causation. Hence, it has been defined that introducing interprofessional teams is indispensible to enhancing client-caregiver cooperation and strengthening patient-centered care.

Despite the fact that the domain of inferences has not been represented in the studies by Parascandola and Weed (2001), this criteria is crucial for defining the biases of causation and enhancing the accuracy of a cause-and-effect model. In this respect, the primary hypothesis and the statistical data deprived from biases have coincided to meet the goals of the study. In order to check the findings for validity, Legare et al. (2011) have introduced solid theoretical and empirical supports and have applied to me methods that were previously tested and used in other scientific studies. This fills in the requirements of the stage of journals. The final stage of causation refers to domain of meta-analysis knowledge encompassing modified and independent causes of informed consent and proper client-centered care. Judging from the above presented analysis of causation represented in the studies by Legare et al. (2011), the research has turned out to be valid and reliable because it has managed to run through the domains of causes and effects. Inferences of biases, therefore, have not distorted the information and enabled the researchers to understand how various theoretical frameworks can be applied in practice.

References

Legare, F., et al. (2011). A Conceptual Framework for Interprofessional Shared Decision Making in Home Care: Protocol for a Feasibility Study. Health Services Research, 11(23), 1-7.

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Maclure, M., & Schneeweiss, S. (2001) Causation of Bias: The Episcope, Epidemiology, 12(1), 114-122.

Parascandola, M., & Weed, D. L. (2001). Causation in Epidemiology. Journal of Epidemiology and Community Health, 55(12), 905-912.

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1. StudyCorgi. "Definitions of Causation: Analysis of New Areas." December 26, 2021. https://studycorgi.com/definitions-of-causation-analysis-of-new-areas/.


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StudyCorgi. (2021) 'Definitions of Causation: Analysis of New Areas'. 26 December.

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