Causal 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.
Understanding cause and effect Causal relationships may be understood as a transfer of force. If A causes B, then A must transmit a force (or causal power) to B which results in the effect. Causal relationships suggest change over time; cause and effect are temporally related, and the cause precedes the outcome.
Causality may also be inferred in the absence of a force, through the notion of contingency. A cause can be removal (or stopping), like removing a support from a structure and causing a collapse or a lack of precipitation causing wilted plants.
Humans can reason about many topics (for example, in social and counterfactual situations and in the experimental sciences) with the aid of causal understanding. Understanding depends on the ability to comprehend cause and effect. People must be able to reason about the causes of others’ behavior (to understand their intentions and act appropriately) and understand the likely effects of their own actions. Counterfactual arguments are presented in many situations; humans are predisposed to think about “what might have been”, even when that argument has no bearing on the current situation.
Cause-and-effect relationships define categories of objects. Wings are a feature of the category "birds"; this feature is causally interconnected with another feature of the category, the ability to fly.
Traditionally, research in cognitive psychology has focused on causal relations when the cause and the effect are both binary values; both the cause and the effect are present or absent.Jenkins, H. M., & Ward, W. C. (1965). Judgment of contingency between responses and outcomes. Psychological Monographs, 79, ( 1, Whole No. 594).Cheng, P. W. (1997). From covariation to causation: A causal power theory. Psychological Review, 104, 367–405. It is also possible that both the cause and the effect take continuous values. For example, turning the volume knob of a radio (as the cause) increases or decreases the sound intensity (as the effect). In these cases, the relation between the variables of the cause and the effect resembles a mathematical function in which change in the variable of the cause changes values in the variable of the effect. Human learning of such relations has been studied in the field of "Function Learning".Carroll, J. D. (1963). Functional learning: The learning of continuous functional mappings relating stimulus and response continua. NJ: Education Testing Service PrincetonBrehmer, B. (1974). Hypotheses about relations between scaled variables in the learning of probabilistic inference tasks. Organizational Behavior and Human Performance, 11, 1–27.Koh, K., & Meyer, D. E. (1991). Function learning: Induction of continuous stimulus-response relations. Journal of Experimental Psychology: Learning, Memory, and Cognition, 17, 811–836.
Even so, it is well understood that physical applications of continuous mathematical models are not literally continuous in practice.Maddy, Penelope (2017). Set-Theoretic Foundations A knob on a radio does not take on an uncountably infinite number of possible values—it takes a finite number of possible values fully limited by the mechanical, physical, nature of the knob itself. There exists no one-to-one mapping between the continuous mathematics used for engineering applications and the physical product(s) produced by the engineering. Indeed, this is a prominent problem within Philosophy of Mathematics.Eklund, Matti (2019). Stanford Encyclopedia of Philosophy: Fictionalism Balaguer, Mark (2018). Stanford Encyclopedia of Philosophy: Fictionalism in the Philosophy of Mathematics One possible answer to this open question is that reality is rasterized (possibly at the Planck Scale, see Loop Quantum Gravity) and is fundamentally discrete. So goes the theory of mathematical fictionalism, where continuous mathematics serves as a fictional construct of imagery used for reasoning geometrically via drawings and intuitive ideas of shapes absent of measurement data.
Cause and effect may also be understood probabilistically, via inferential statistics, where the distinction between correlation and causation is important. Just because two variables are correlated does not mean that one caused the other. For example, ice cream sales are correlated with the number of deaths due to drowning. This is not because ice cream causes drowning or because drowning deaths cause people to buy ice cream. Rather, it is because a third factor causes both. In this case, hot weather causes people both to buy ice cream and to go swimming, and the latter increasing the chances of drowning. These other possible causes that can account for the correlation between two variables are called confounding variables.
In this way, ascertaining cause and effect relations is quite hard and arguably impossible through statistical observation alone. Statistical studies can alleviate the problem by controlling for variables suspected to be confounders, but it is still possible that an observed correlation is caused by some uncontrolled-for factor. The scientific method is a solution to this problem. In a scientific experiment, the experimenters vary an independent variable and observe the changes in the dependent variable. As long as the independent variable is varied in a random way across the sample (e.g., in a medical study, half of the participants may be chosen randomly to receive the treatment, and the other half a placebo), there will be no confounding variables that cause both the change in the independent and dependent variables, since the independent variable is controlled by the experimenters.
Causality is an important question in modern physics. According to deterministic theories, any future event could in principle be predicted with perfect knowledge of the present, since one could precisely calculate what outcome would be caused by the present state of affairs. However, quantum mechanics has brought back the possibility of indeterministic events - events that are not determined by prior causes. Whether the outcomes of quantum-mechanical events are really indeterminstic is one of the biggest open problems in physics today and is part of the interpretation of quantum physics and its reconciliation with the causal structure of special relativity.
Theories of causality also play important roles in debates about free will. For example, if determinism is true, it implies that our actions are caused by prior events, which incompatibilists argue is inconsistent with free will. As a result, incompatibilists fall into two main camps: libertarians (not to be confused with political libertarians), who argue that human actions are not determined by prior causes, and hard determinists, who argue that free will does not exist. The main challenge for libertarian philosophers is to explain how human actions are caused, if they are not caused by prior events. Some cite quantum mechanics as evidence that human actions may not be deterministic.Balaguer, Mark. (2010). MIT Press: Free Will as an Open Scientific Problem Hameroff, Stewart. (2012). Front Integr Neurosci.:How quantum brain biology can rescue conscious free will. In opposition to both libertarians and hard determinists are compatiblists, who argue that the existence of free will is compatible with determinism.
Inferring cause and effect Humans are predisposed to understand cause and effect, making inferences bi-directionally. Temporal cues demonstrate causality. When observing an event, people assume that things preceding the event cause it, and things following the event are effects of it.
Coincidence of movement and spatial relationships are another way to infer cause and effect. If objects move together (or one object seems to initiate the movement of another), causality is inferred from that relationship. Animacy may also be inferred from such relationships.
Causal reasoning may be activated almost automatically. However, inferences about cause and effect do not always demonstrate understanding of mechanisms underlying causality; causality has been described as "cognitive illusion". Much understanding of cause and effect is based on associations, without an understanding of how events are related to one another.
A 2013 neuropsychology study demonstrates that humans conform new information to old information. This suggests an inverted causal experience: cause must be attributed to effect a posteriori to understand the causal connection between agent and act. Friedrich Nietzsche argued against Aristotelian causality (that cause precedes effect) in The Will To Power.Friedrich Nietzsche. The Will To Power. circa 1880s, which recent neuropsychological experiments confirm.
477 (Nov. 1887-March 1888)
"I maintain the phenomenality of the inner world, too: every- thing of which we become conscious is arranged, simplified, schematized, interpreted through and through — the actual process of inner "perception," the causal connection between thoughts, feelings, desires, between subject and object, are absolutely hidden from us — and are perhaps purely imaginary. The "apparent inner world" is governed by just the same forms and procedures as the "outer" world. We never encounter "facts": pleasure and displeasure are subsequent and derivative intellectual phenomena —
"Causality" eludes us; to suppose a direct causal link between thoughts, as logic does — that is the consequence of the crudest and clumsiest observation. Between two thoughts all kinds of affects play their game: but their motions are too fast, therefore we fail to recognize them, we deny them —
"Thinking" as epistemologists conceive it, simply does not occur: it is a quite arbitrary fiction, arrived at by selecting one element from the process and eliminating all the rest, an artificial arrangement for the purpose of intelligibility —
The "spirit," something that thinks: where possible even "absolute, pure spirit" – this conception is a second derivative of that false introspection which believes in "thinking": first an act is imagined which simply does not occur, "thinking," and secondly a subject-substratum in which every act of thinking, and nothing else, has its origin: that is to say, both the deed and the doer are fictions.
Humans understand cause and effect. Research suggests that other animals, such as rats and monkeys, may or may not understand cause and effect. Animals may use information about cause and effect to improve decision-making and make inferences about past and future events. A constant which guides human reasoning and learning about events is causality. Causal considerations are integral to how people reason about their environment.Sloman, S. A. (2005). Causal models. New York, NY: Oxford University Press. Humans use causal cues and their related effects to make decisions and predictions and to understand mechanisms leading to change.Hagmayer, Y., Sloman, S. A., Lagnado, D. A., & Waldmann, M. R. (2007). "Causal reasoning through intervention." In A. Gopnik & L. Schulz (Eds.), Causal learning: Psychology, philosophy, and computation (pp. 86–100). Oxford, England: Oxford University Press.
Types of causal relationships Several types of causal models are developed as a result of observing causal relationships: common-cause relationships, common-effect relationships, causal chains and causal homeostasis.
In common-cause relationships, a single cause has several effects: