Causal explanations usually depend on a number of assumptions concerning physical laws.
The term argument implies a difference of opinion. If everyone agrees, then there is no argument. So a causal explanation may or may be an argument.
Causal generalization is a type of deductive reasoning in which an accepted casual correlation is applied to a specific. This type of argument is commonly used to support a claim of explanation. For example, Oreo cookies make children hungry therefore, these other off brand sandwich cookies will make children hungry.
a scientific explanation of the total causal relationships of an assemblage of phenomena that are mutually coordinated but not subordinated at places.
I think it would be a derivative controller.
The children's book If You Give a Mouse A Cookie is a great example of a causal chain. Though the ideas are silly (meant for entertaining children), it still shows how A leads to B and B leads to C...
A causal story is an explanation of events or outcomes that emphasizes the relationships between different factors or variables, highlighting how one factor leads to the occurrence of another. It aims to narrate how specific causes result in particular effects or consequences. Causal stories help understand the mechanics and relationships behind phenomena and are commonly used in scientific research and analysis.
A causal hypothesis is a research that predicts cause and effects among variables to be studied and their relationships in arousal levels and performance.
explanation
A SYSTEM Iis said to be causal if the present valkue cof the output siugnal depends only on the present and past values of the input signal.examples of causal systems 1.y[n]=2(x[n]+x[n-1]+x[n-2]); 2.it is example of non causal system y[n]=x[n+1]; A system is said to be causal if the present value of the output signal depends only on the present and past values of the input signal.examples of causal systems 1.y[n]=2(x[n]+x[n-1]+x[n-2]); 2.it is example of non causal system y[n]=x[n+1];
a causal conjunction is 'because'
The standard answer is that a positive statistical correlation, no matter how strong, never proves anything about the causal relationship. Technically, correlations are symmetric and so the evidence is identical whether you imagine that A causes B or B causes A. Another problem is that you could have an omitted third factor C which explains both A and B. A correlation between A and B never rules out the possibility of C influencing them both. What you can sometimes say more realistically is that a strong correlation might make a proposed causal explanation more plausible. It might be evidence as part of an argument, but it's not sufficient by itself. Other parts of the argument could be exclusion of other factors (through experiments or statistical controls) and logical precedence. For example, if you had evidence that women are smarter than men, it doesn't seem likely that smartness causes gender. Similarly, events from the future don't influence events of the past; thus establishing the time sequence might also help to build a causal explanation. In short, there are few if any obvious causal relationships based on correlation alone if you want to use rigorous methods. Experiments and replication of results under diverse circumstances are the best way to show a causal relationship.
are. Causal Explanations arguments