Causal Effect

… a core concept in Quantitative Methods and Atlas104

Concept description

Study.com (reference below) defines causal effect as “something has happened, or is happening, based on something that has occurred or is occurring.”

Study.com elaborates:

“The term causal effect is used quite often in the field of research and statistics. There are two terms involved in this concept: 1) causal and 2) effect. When you look at both of these terms first individually and then together, the overall concept is easy to understand!

“Let’s look at the first word: causal. The root of this first word is cause. In order to produce something, there must be some type of cause to the situation, or there must be a reason why something is happening (referred to as the outcome). Now, keep this in mind as you look at the second word.

“The second word is ‘effect.’ ‘Effect’ is usually brought on by a cause. Therefore, causal effect means that something has happened, or is happening, based on something that has occurred or is occurring. A simple way to remember the meaning of causal effect is: B happened because of A, and the outcome of B is strong or weak depending how much of or how well A worked.”

Causal reasoning

iSTAR Assessment (reference below) provides a description of the related term, causal reasoning:

“Causal reasoning relates to establishing the presence of causal relationships among events. When causal relationships exist, there is good reason to believe that events of one type (the causes) are systematically related to events of another type (the effects). …

“Causal reasoning relates to establishing the presence of causal relationships among events. When causal relationships exist, we have good reason to believe that events of one type (the causes) are systematically related to events of another type (the effects). It may become possible for us to alter our environment by producing (or by preventing) events if we can identify the causes.

“Most studies of student ability to coordinate theory and evidence focus on what is best described as inductive causal inference (i.e., given a pattern of evidence, what inferences can be drawn?).

“If there is a causal relationship between variables x and y, there can be several kinds of causes:

Necessary causes: If x is a necessary cause of y, then the presence of y necessarily implies the presence of x with a probability of 100%. The presence of x, however, does not imply that y will occur.

Sufficient causes: If x is a sufficient cause of y, then the presence of x necessarily implies the presence of y with the probability of 100%. However, another cause z may alternatively cause y. Thus the presence of y does not imply the presence of x. For example, if it is sunny outside, then it is daytime. It being sunny is a sufficient cause for one to conclude that it is daytime. But just because it is daytime does not necessarily mean it is sunny outside.

Contributory causes: If x is a contributory cause of y, it means the presence of x makes possible the presence of y, but not with the probability of 100%. In other words, a contributory cause may be neither necessary nor sufficient but it must be contributory. For example, stubbing my toe causes pain. Stubbing my toe is a contributory cause to being in pain because I could be in pain from a headache or sore throat instead.”

Sources

Study.com, Causal Effect – Definition & Overview, at https://study.com/academy/lesson/causal-effect-definition-lesson-quiz.html#transcriptHeader, accessed 12 May 2018.

iSTAR Assessment, Causal Reasoning, at http://www.istarassessment.org/srdims/causal-reasoning-2/, accessed 12 May 2018.

Topic, subject and Atlas course

Research Design (core topic) in Quantitative Methods and Atlas104.

Page created by: Alec Wreford and Ian Clark, last modified 12 May 2018.

Image: Coursera, University of Pennsylvania, at https://www.coursera.org/penn, accessed 12 May 2018.