causality

(noun)

the relationship between an event (the cause) and a second event (the effect), where the second event is understood as a consequence of the first

Related Terms

Examples of causality in the following topics:

  • Correlation is Not Causation

    • The conventional dictum "correlation does not imply causation" means that correlation cannot be used to infer a causal relationship between variables.
    • This dictum does not imply that correlations cannot indicate the potential existence of causal relations.
    • Consequently, establishing a correlation between two variables is not a sufficient condition to establish a causal relationship (in either direction).
    • A few go further and calculate the likelihood of a true causal relationship.
    • Examples include the Granger causality test and convergent cross mapping.

  • Koch's Postulates

    • Koch's postulates are four criteria designed in the 1880's to establish a causal relationship between a causative microbe and a disease.
    • Koch's postulates are four criteria designed to establish a causal relationship between a causative microbe and a disease.
    • Therefore, while Koch's postulates retain historical importance and continue to inform the approach to microbiologic diagnosis, fulfillment of all four postulates is not required to demonstrate causality.
    • Koch's postulates are four criteria designed in the 1880's to establish a causal relationship between a causative microbe and a disease.

  • Overview of data collection principles exercises

    • Also comment on whether or not the results of the study can be generalized to the population and if the findings of the study can be used to establish causal relationships.
    • Also comment on whether or not the results of the study can be generalized to the population and if the findings of the study can be used to establish causal relationships.
    • However, since the study is observational, the findings do not imply causal relationships.
    • However, since the study is an experiment, the findings can be used to establish causal relationships.
    • (d) Since this is an observational study, a causal relationship is not implied.

  • Introducing observational studies and experiments

    • In general, observational studies can provide evidence of a naturally occurring association between variables, but they cannot by themselves show a causal connection.
    • When researchers want to investigate the possibility of a causal connection, they conduct an experiment.
    • To check if there really is a causal connection between the explanatory variable and the response, researchers will collect a sample of individuals and split them into groups.

  • Statistical Literacy

    • What experimental design involving niacin would test whether the relationship between HDL and heart disease is causal?
    • A finding that niacin increased HDL without decreasing heart disease would cast doubt on the causal relationship.

  • Observational studies

    • Making causal conclusions based on experiments is often reasonable.
    • However, making the same causal conclusions based on observational data can be treacherous and is not recommended.
    • While one method to justify making causal conclusions from observational studies is to exhaust the search for confounding variables, there is no guarantee that all confounding variables can be examined or measured.
    • In the same way, the county data set is an observational study with confounding variables, and its data cannot easily be used to make causal conclusions.
    • However, it is unreasonable to conclude that there is a causal relationship between the two variables.

  • Formulating the Hypothesis

    • This is an example of a causal hypothesis.
    • In this hypothesis, the independent (causal) variable is civic engagement and the dependent variables (or effects) are the qualities of government.
    • A hypothesis will generally provide a causal explanation or propose some association between two variables.
    • For example, if the hypothesis is a causal explanation, it will involve at least one dependent variable and one independent variable.

  • Experimental Epidemiology

    • Experimental epidemiology uses an experimental model to confirm a causal relationship suggested by observational studies.
    • The identification of causal relationships between these exposures and outcomes is an important aspect of epidemiology.
    • Although epidemiology is sometimes viewed as a collection of statistical tools used to elucidate the associations of exposures to health outcomes, a deeper understanding of this science is that of discovering causal relationships.

  • Causation

    • An obvious obstacle to inferring causality is that there are many unmeasured variables that affect how many hours someone sleeps.
    • Does the possibility of a third-variable problem make it impossible to draw causal inferences without doing an experiment?
    • However, be aware that the assumption of no third-variable problem may be hidden behind a complex causal model that contains sophisticated and elegant mathematics.
    • A second problem is determining the direction of causality.

  • Regression Analysis for Forecast Improvement

    • Regression Analysis is a causal / econometric forecasting method that is widely used for prediction and forecasting improvement.
    • Regression Analysis is a causal / econometric forecasting method.
    • In restricted circumstances, regression analysis can be used to infer causal relationships between the independent and dependent variables.
    • However, in many applications, especially with small effects or questions of causality based on observational data, regression methods give misleading results.