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Process tracing is a qualitative research method used to develop and test theories. [1] [2] [3] Process-tracing can be defined as the following: it is the systematic examination of diagnostic evidence selected and analyzed in light of research questions and hypotheses posed by the investigator (Collier, 2011).
The Bradford Hill criteria, otherwise known as Hill's criteria for causation, are a group of nine principles that can be useful in establishing epidemiologic evidence of a causal relationship between a presumed cause and an observed effect and have been widely used in public health research.
Causal research, is the investigation of (research into) cause-relationships. [ 1 ] [ 2 ] [ 3 ] To determine causality, variation in the variable presumed to influence the difference in another variable(s) must be detected, and then the variations from the other variable(s) must be calculated (s).
Causal inference is the process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system. The main difference between causal inference and inference of association is that causal inference analyzes the response of an effect variable when a cause of the effect variable is changed.
Designing Social Inquiry: Scientific Inference in Qualitative Research (or KKV) is an influential 1994 book written by Gary King, Robert Keohane, and Sidney Verba that lays out guidelines for conducting qualitative research. [1] The central thesis of the book is that qualitative and quantitative research share the same "logic of inference."
Judea Pearl defines a causal model as an ordered triple ,, , where U is a set of exogenous variables whose values are determined by factors outside the model; V is a set of endogenous variables whose values are determined by factors within the model; and E is a set of structural equations that express the value of each endogenous variable as a function of the values of the other variables in U ...
When experimental interventions are infeasible or illegal, the derivation of a cause-and-effect relationship from observational studies must rest on some qualitative theoretical assumptions, for example, that symptoms do not cause diseases, usually expressed in the form of missing arrows in causal graphs such as Bayesian networks or path diagrams.
Causal analysis is the field of experimental design and statistics pertaining to establishing cause and effect. [1] Typically it involves establishing four elements: correlation, sequence in time (that is, causes must occur before their proposed effect), a plausible physical or information-theoretical mechanism for an observed effect to follow from a possible cause, and eliminating the ...