Causal modelIn the philosophy of science, a causal model (or structural causal model) is a conceptual model that describes the causal mechanisms of a system. Several types of causal notation may be used in the development of a causal model. Causal models can improve study designs by providing clear rules for deciding which independent variables need to be included/controlled for. They can allow some questions to be answered from existing observational data without the need for an interventional study such as a randomized controlled trial.
Causal reasoningCausal reasoning is the process of identifying causality: the relationship between a cause and its effect. The study of causality extends from ancient philosophy to contemporary neuropsychology; assumptions about the nature of causality may be shown to be functions of a previous event preceding a later one. The first known protoscientific study of cause and effect occurred in Aristotle's Physics. Causal inference is an example of causal reasoning. Causal relationships may be understood as a transfer of force.
Action potentialAn action potential occurs when the membrane potential of a specific cell rapidly rises and falls. This depolarization then causes adjacent locations to similarly depolarize. Action potentials occur in several types of animal cells, called excitable cells, which include neurons, muscle cells, and in some plant cells. Certain endocrine cells such as pancreatic beta cells, and certain cells of the anterior pituitary gland are also excitable cells.
Causal loop diagramA causal loop diagram (CLD) is a causal diagram that aids in visualizing how different variables in a system are causally interrelated. The diagram consists of a set of words and arrows. Causal loop diagrams are accompanied by a narrative which describes the causally closed situation the CLD describes. Closed loops, or causal feedback loops, in the diagram are very important features of CLDs. The words with arrows coming in and out represent variables, or quantities whose value changes over time and the links represent a causal relationship between the two variables (i.
ExperimentAn experiment is a procedure carried out to support or refute a hypothesis, or determine the efficacy or likelihood of something previously untried. Experiments provide insight into cause-and-effect by demonstrating what outcome occurs when a particular factor is manipulated. Experiments vary greatly in goal and scale but always rely on repeatable procedure and logical analysis of the results. There also exist natural experimental studies.
Spike-and-waveSpike-and-wave is a pattern of the electroencephalogram (EEG) typically observed during epileptic seizures. A spike-and-wave discharge is a regular, symmetrical, generalized EEG pattern seen particularly during absence epilepsy, also known as ‘petit mal’ epilepsy. The basic mechanisms underlying these patterns are complex and involve part of the cerebral cortex, the thalamocortical network, and intrinsic neuronal mechanisms. The first spike-and-wave pattern was recorded in the early twentieth century by Hans Berger.
CausalityCausality (also called causation, or cause and effect) is influence by which one event, process, state, or object (a cause) contributes to the production of another event, process, state, or object (an effect) where the cause is partly responsible for the effect, and the effect is partly dependent on the cause. In general, a process has many causes, which are also said to be causal factors for it, and all lie in its past. An effect can in turn be a cause of, or causal factor for, many other effects, which all lie in its future.
Object detectionObject detection is a computer technology related to computer vision and that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. Well-researched domains of object detection include face detection and pedestrian detection. Object detection has applications in many areas of computer vision, including and video surveillance. It is widely used in computer vision tasks such as , vehicle counting, activity recognition, face detection, face recognition, video object co-segmentation.
Adverse effectAn adverse effect is an undesired harmful effect resulting from a medication or other intervention, such as surgery. An adverse effect may be termed a "side effect", when judged to be secondary to a main or therapeutic effect. The term complication is similar to adverse effect, but the latter is typically used in pharmacological contexts, or when the negative effect is expected or common. If the negative effect results from an unsuitable or incorrect dosage or procedure, this is called a medical error and not an adverse effect.
Placebo-controlled studyPlacebo-controlled studies are a way of testing a medical therapy in which, in addition to a group of subjects that receives the treatment to be evaluated, a separate control group receives a sham "placebo" treatment which is specifically designed to have no real effect. Placebos are most commonly used in blinded trials, where subjects do not know whether they are receiving real or placebo treatment. Often, there is also a further "natural history" group that does not receive any treatment at all.
