Causal reasoning

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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. [1] Causal inference is an example of causal reasoning.

Contents

Understanding cause and effect

Causal relationships may be understood as a transfer of force. [2] 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. [3]

Causality may also be inferred in the absence of a force, a less-typical definition. [4] 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. [3] 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. [5] Wings are a feature of the category "birds"; this feature is causally interconnected with another feature of the category, the ability to fly. [5]

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. [6] [7] 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". [8] [9] [10]

Even so, it is well understood that physical applications of continuous mathematical models are not literally continuous in practice. [11] 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. [12] [13] 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. [14] [15] 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. [16] When observing an event, people assume that things preceding the event cause it, and things following the event are effects of it. [17]

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. [18] Animacy may also be inferred from such relationships.

Causal reasoning may be activated almost automatically. [3] [19] However, inferences about cause and effect do not always demonstrate understanding of mechanisms underlying causality; causality has been described as "cognitive illusion". [20] Much understanding of cause and effect is based on associations, without an understanding of how events are related to one another. [21]

A 2013 neuropsychology study [22] 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. [23]

Humans understand cause and effect. Research suggests that other animals, such as rats [24] and monkeys, [25] 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. [26] A constant which guides human reasoning and learning about events is causality. [27] Causal considerations are integral to how people reason about their environment. [3] Humans use causal cues and their related effects to make decisions and predictions and to understand mechanisms leading to change. [28]

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. [21]

Example of a single cause with multiple effects Common cause example.png
Example of a single cause with multiple effects
A virus is an example of a single cause resulting in several effects (fever, headache and nausea).
Example of multiple causes with a single effect Example of common effect.png
Example of multiple causes with a single effect
An increase in government spending is an example of one effect with several causes (reduced unemployment, decreased currency value, and increased deficit).
Example of a causal chain Example of linear chain.png
Example of a causal chain
An example is poor sleep leading to fatigue, which leads to poor coordination.
Example of causal homeostasis Example of causal homeostasis.png
Example of causal homeostasis
Feathers, hollow bones, high metabolic rate and flight reinforce each other in birds, with adaptation to the whole rather than one instance beginning a causal relationship. [21]

Types of causal reasoning

While causal understanding can be automatic, in complex situations advanced reasoning is necessary. Types of causal reasoning [2] include:

Deduction

Deductive reasoning implies a general rule; an event is a guaranteed conclusion. An outcome may be deduced based on other arguments, which may determine a cause-and-effect relationship.

Induction

Inductive reasoning is an inference made with uncertainty; the conclusion is likely, but not guaranteed. Induction can be used to speculate about causality.

Abduction

In abductive reasoning, the premises do not guarantee a conclusion. Abduction moves from data description to a hypothesis without a necessary relationship between cause and effect.

Models

There are several models of how humans reason about causality.

Dependency

The dependency model asserts that effects are contingent upon causes; [3] cause and effect have a probable relationship.

Covariation

The covariation (regularity) model, a type of dependency model, suggests that humans understand relationships between causes and effects by their coincidence, inferring that change in a cause changes an effect. [29]

Mechanism

This model [2] suggests that cause and effect are mechanistically related. In this situation, there is a basic process underlying the cause and effect.

Dynamics

This model of causal representation [30] suggests that causes are represented by a pattern of forces. The force theory [31] is an extension of the dynamics model that applies to causal representation and reasoning (i.e., drawing inferences from the composition of multiple causal relations).

Development in humans

Children develop an ability to understand causality and make inferences based on cause and effect at an early age; [19] some research suggests that children as young as eight months can understand cause and effect. [32] An understanding of mechanism and causality go hand in hand; children need to understand cause and effect to understand the operation of mechanisms, which allows them to understand causal relationships. [21] Children ask "why?" at an early age to understand mechanism and, in turn, causality. A child's first "why" question often coincides with their first attempt to explain something, within the first year after acquiring language. [21] Children ask "why" to understanding mechanism and causality. [33]

The ability to understand and reason about causality at a young age allows children to develop naïve theories about many topics. Causality helps children learn about physics, language, concepts and the behavior of others. [19] There is a developmental pattern to the causal understanding children have. [33]

Infants have an understanding of causal power. [21] They know that certain causes have particular effects. Young children, from late infancy to early childhood, understand functional relations: [21] a particular property (or component of a mechanism) has a certain function. They also understand causal density: how causes can interact in a complex way.

Older children and adults continue to develop an understanding of mechanistic fragments. [21] They understand the components of a working system in isolation, although the full mechanistic details of a system do not emerge until adulthood. Jean Piaget defined preoperational, concrete operational and formal operational stages of development.

Across cultures

Causal attributions have been shown to be dissimilar among different cultures in several ways:

Causal attributions

Yan and Gaier [34] investigated causal attributions of college success and failure between two groups of students, American and Asian. The Asian group was from China, Korea, Japan and Southeast Asia. Performance was similar across the four nationalities. The students were asked to make judgments about someone else's successes and failures in schoolwork, and whether those outcomes were attributable to innate ability or to expended effort. American participants were much more likely to attribute academic achievement to ability than Asian participants were. Although Americans tended to rate success as attributable to effort, failure was not perceived as being a result of a lack of effort. Asian students did not show this pattern.

Comparisons between Western and Eastern children and adults suggest differences between the cultures in the causality attributable to particular illnesses. [35] After reading stories of illnesses and making inferences about the causes of those illnesses, both groups showed an understanding of the biological causes of most illnesses. However, all the children and the Eastern adults also attributed some illnesses (and their remedies) to magical causes.

Causal motivations

Members of individualist or collectivist cultures may make different attributions of the origins and motivations of movement on a small scale among animated objects, or what would cause movement within a group of animated objects. [36] Participants from the UK, China and Hong Kong were shown videos of animated fish on a computer screen. The videos depicted a central fish moving toward or away from a group of fish, and participants were asked to determine the relationship among the fish: internally motivated (the central fish was looking for food) or externally motivated (the central fish wanted to join the others). Another set of videos suggested that the group of fish was the predominant agent, with the individual fish being acted upon. These different videos provided an opportunity to determine whether group or individual action is the preferred motivating force among different cultures.

Self-reported results suggested that Asian participants preferred descriptions and situations where the group was the central focus and causal agent, while Westerners preferred situations in which the individual was the agent. These effects also extended to memory processes; collectivist participants had better memories of situations in which the group was primary. This suggests that members of individualistic cultures are more responsive to independent agents and members of collectivist cultures are more responsive when groups guide individual action.

Causal reasoning in non-human animals

Causal reasoning is not unique to humans; animals are often able to use causal information as cues for survival. [20] Rats are able to generalize causal cues to gain food rewards. Animals such as rats can learn the mechanisms required for a reward by reasoning about what could elicit a reward. [20]

New Caledonian Crow (Corvus moneduloides) CorvusMoneduloidesKeulemans.jpg
New Caledonian Crow (Corvus moneduloides)

New Caledonian crows have been studied for their ability to reason about causal events. This intelligent species uses tools in a way that even chimpanzees cannot, making complex tools to bring food within reach. [37]

Experimental work with this species suggests that they can understand hidden causes in a way that was previously believed uniquely human. [38] In the first of two experiments a crow was confined, with food in a tube inaccessible to the crow without some effort. A human entered the enclosure and went behind a curtain, waving a stick near the food tube through a hole in the curtain. When the human left the enclosure the crow confidently moved toward the food area and retrieved the reward, knowing that the human cause of the moving stick (albeit invisible) was gone. In the second experiment, no human entered or exited the enclosure. In this case the crow moved toward the food uncertainly, not knowing what caused the stick to move.

See also

Related Research Articles

Causality is an influence by which one event, process, state, or object (acause) contributes to the production of another event, process, state, or object (an effect) where the cause is at least partly responsible for the effect, and the effect is at least partly dependent on the cause. The cause of something may also be described as the reason for the event or process.

A Bayesian network is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). While it is one of several forms of causal notation, causal networks are special cases of Bayesian networks. Bayesian networks are ideal for taking an event that occurred and predicting the likelihood that any one of several possible known causes was the contributing factor. For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases.

In philosophy of mind and cognitive science, folk psychology, or commonsense psychology or naïve psychology, is a human capacity to explain and predict the behavior and mental state of other people. Processes and items encountered in daily life such as pain, pleasure, excitement, and anxiety use common linguistic terms as opposed to technical or scientific jargon. Folk psychology allows for an insight into social interactions and communication, thus stretching the importance of connection and how it is experienced.

Moral reasoning is the study of how people think about right and wrong and how they acquire and apply moral rules. It is a subdiscipline of moral psychology that overlaps with moral philosophy, and is the foundation of descriptive ethics.

In psychology, an attribution bias or attributional errors is a cognitive bias that refers to the systematic errors made when people evaluate or try to find reasons for their own and others' behaviors. It refers to the systematic patterns of deviation from norm or rationality in judgment, often leading to perceptual distortions, inaccurate assessments, or illogical interpretations of events and behaviors.

<span class="mw-page-title-main">Granger causality</span> Statistical hypothesis test for forecasting

The Granger causality test is a statistical hypothesis test for determining whether one time series is useful in forecasting another, first proposed in 1969. Ordinarily, regressions reflect "mere" correlations, but Clive Granger argued that causality in economics could be tested for by measuring the ability to predict the future values of a time series using prior values of another time series. Since the question of "true causality" is deeply philosophical, and because of the post hoc ergo propter hoc fallacy of assuming that one thing preceding another can be used as a proof of causation, econometricians assert that the Granger test finds only "predictive causality". Using the term "causality" alone is a misnomer, as Granger-causality is better described as "precedence", or, as Granger himself later claimed in 1977, "temporally related". Rather than testing whether Xcauses Y, the Granger causality tests whether X forecastsY.

<span class="mw-page-title-main">Confounding</span> Variable or factor in causal inference

In causal inference, a confounder is a variable that influences both the dependent variable and independent variable, causing a spurious association. Confounding is a causal concept, and as such, cannot be described in terms of correlations or associations. The existence of confounders is an important quantitative explanation why correlation does not imply causation. Some notations are explicitly designed to identify the existence, possible existence, or non-existence of confounders in causal relationships between elements of a system.

<span class="mw-page-title-main">Mediation (statistics)</span> Statistical model

In statistics, a mediation model seeks to identify and explain the mechanism or process that underlies an observed relationship between an independent variable and a dependent variable via the inclusion of a third hypothetical variable, known as a mediator variable. Rather than a direct causal relationship between the independent variable and the dependent variable, a mediation model proposes that the independent variable influences the mediator variable, which in turn influences the dependent variable. Thus, the mediator variable serves to clarify the nature of the causal relationship between the independent and dependent variables.

Attribution is a term used in psychology which deals with how individuals perceive the causes of everyday experience, as being either external or internal. Models to explain this process are called Attribution theory. Psychological research into attribution began with the work of Fritz Heider in the early 20th century, and the theory was further advanced by Harold Kelley and Bernard Weiner. Heider first introduced the concept of perceived 'locus of causality' to define the perception of one's environment. For instance, an experience may be perceived as being caused by factors outside the person's control (external) or it may be perceived as the person's own doing (internal). These initial perceptions are called attributions. Psychologists use these attributions to better understand an individual's motivation and competence. The theory is of particular interest to employers who use it to increase worker motivation, goal orientation, and productivity.

The theory-theory is a scientific theory relating to the human development of understanding about the outside world. This theory asserts that individuals hold a basic or 'naïve' theory of psychology to infer the mental states of others, such as their beliefs, desires or emotions. This information is used to understand the intentions behind that person's actions or predict future behavior. The term 'perspective taking' is sometimes used to describe how one makes inferences about another person's inner state using theoretical knowledge about the other's situation.

Patricia Wenjie Cheng is a Chinese American psychologist. She is a leading researcher in cognitive psychology who works on human reasoning. She is best known for her psychological work on human understanding of causality. Her "power theory of the probabilistic contrast model," or power PC theory (1997) posits that people filter observations of events through a basic belief that causes have the power to generate their effects, thereby inferring specific cause-effect relations.

The psychology of reasoning is the study of how people reason, often broadly defined as the process of drawing conclusions to inform how people solve problems and make decisions. It overlaps with psychology, philosophy, linguistics, cognitive science, artificial intelligence, logic, and probability theory.

Causal analysis is the field of experimental design and statistics pertaining to establishing cause and effect. Typically it involves establishing four elements: correlation, sequence in time, a plausible physical or information-theoretical mechanism for an observed effect to follow from a possible cause, and eliminating the possibility of common and alternative ("special") causes. Such analysis usually involves one or more artificial or natural experiments.

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. The study of why things occur is called etiology, and can be described using the language of scientific causal notation. Causal inference is said to provide the evidence of causality theorized by causal reasoning.

In statistics, econometrics, epidemiology, genetics and related disciplines, causal graphs are probabilistic graphical models used to encode assumptions about the data-generating process.

The Perruchet effect is a psychological phenomenon in which a dissociation is shown between conscious expectation of an event and the strength or speed of a response to the event. This can be demonstrated by sequential analyses of consecutive trials such as eye blinking conditioning, electrodermal shocks and cued go/no-go task. The dissociation design differentiates the automatic associative strength and propositional expectation's effects on associative learning and conditioning, i.e., the cognitive learning process with relationship between events.

Intuitive statistics, or folk statistics, is the cognitive phenomenon where organisms use data to make generalizations and predictions about the world. This can be a small amount of sample data or training instances, which in turn contribute to inductive inferences about either population-level properties, future data, or both. Inferences can involve revising hypotheses, or beliefs, in light of probabilistic data that inform and motivate future predictions. The informal tendency for cognitive animals to intuitively generate statistical inferences, when formalized with certain axioms of probability theory, constitutes statistics as an academic discipline.

Causal analysis is the field of experimental design and statistical analysis pertaining to establishing cause and effect. Exploratory causal analysis (ECA), also known as data causality or causal discovery is the use of statistical algorithms to infer associations in observed data sets that are potentially causal under strict assumptions. ECA is a type of causal inference distinct from causal modeling and treatment effects in randomized controlled trials. It is exploratory research usually preceding more formal causal research in the same way exploratory data analysis often precedes statistical hypothesis testing in data analysis

The proportionality bias, also known as major event/major cause heuristic, is the tendency to assume that big events have big causes. It is a type of cognitive bias and plays an important role in people's tendency to accept conspiracy theories. Academic psychologist Rob Brotherton summarises it as "When something big happens, we tend to assume that something big must have caused it".

Causal AI is a technique in artificial intelligence that builds a causal model and can thereby make inferences using causality rather than just correlation. One practical use for causal AI is for organisations to explain decision-making and the causes for a decision.

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