The Causal Revolution as the Summit of Scientific-Technological-Industrial Revolutions

It is common sense that modern science and technology and the Industrial Revolutions, involving technological, socioeconomic, political, psychological, and cultural changes, are causally interconnected as complex entangled heterogeneous dynamic causal networks. 

We have a series of scientific, technological, cultural, and industrial revolutions, while ignoring the causal revolution in our mentality, sciences, technologies and industries.

Our very existence depends on causality as a master principle and universal law, but we still debate if causality exists, dividing in various groups, believers or non-believers, agnostics and gnostics, realists, conceptualists and nominalists, theists and atheists, etc.

In all, causality and its causation has not advanced much since Aristotle's ideas of four causes, especially in terms of complexity and nonlinearity. What we have now, is more formalized and much narrower simplistic statistical linear causal models, as presented in Judea Pearl and Dana Mackenzie’s The Book of Why. The New Science of Cause and Effect

Unlike the simple user-friendly artificial models, real causality is hyper-complex, interactive, productive, determinate and stochastic, nonlinear and multi-causal, emergent and omni-directional, top-down and bottom-up, reversed, inverse, inverted, reciprocal, reflexive and symmetrical.

Causality has the absolute priority of ontological existence, even recognized by religion, the Creator is the Great First Cause of all things, but we collectively pretend to understand it devising all sorts of simplified naive linear causal models.

Causality involves the most critical categories, as all infinite universes or world or reality with its thing, entity, substance, state, change, relation, space, time, or agents, causes, processes, effects, and forces, that together embrace everything existing and predictable.

The main features involved in the Causal Revolution are disruptive scientific and technological changes, as in:

A comprehensive, consistent and coherent causal model of the world for humans and computers;

The Master Algorithm of Reality as Descriptive, Deductive, Intuitive, Inductive, Exploratory, Explainable, Predictive and Prescriptive (DDIIEEPP) Platform;

Real AI, Causal Machine Intelligence and Learning, as The Next Big Thing in Technology: Causal/Real AI (RAI) as Leibniz's Superscientist or Laplace's Demon

Causality as the Absolute Principle, and Causation as the Fundamental Cause

Causality is the source and essence of all reality, the soul of the whole world. Its causation is interrelating the things in the world, allowing us to make predictions about the future, explain the past, act on the present, and intervene to change outcomes, to intelligently shape human environments.

 The nature of causality is still a big unknown regardless of it being systematically investigated in many academic disciplines, including philosophy and physics, biology and social sciences, statistics, probability theory, and computer science [Causal Relationship].

As a result, in academia, there are a significant number of theories on causality, physical, biological, mental, social, or informational.

In physics, the cause and the effect are connected through a local mechanism (impact) or a nonlocal mechanism (field), in accordance with known laws of nature.

In causal data science, "data causality" or "causal discovery" makes an exploratory causal analysis, an extension of exploratory data analysis, "the use of statistical algorithms to infer associations in observed data sets that are potentially causal under strict assumptions".

Some social science researchers see causation as not just a correlation, but a counterfactual dependence to manipulate the world, conceptually or experimentally, as a randomized controlled/clinical/comparative trial or intervention study vs. observational study. Randomized experiments are used for the estimation of population-level causal effects while comparing two futures, the "treatment" and the "control". 

Causal modelling as describing the causal mechanisms of all sorts of real-world systems could have a deciding role in AI and ML and DNNs.

The state-of-the art AI is largely big data-driven, but meaningful data analysis is primarily concerned with causal analysis and causal models and estimating causal effects, all pertaining to establishing cause and effect.

Presently, there is a big hype around AI as Machine Learning and Deep Learning, focusing on finding correlations in data sets, classifying data and predicting. It is all instead of understanding and explaining the world, its true causality, with all descriptions, discovering, explanations, prediction, prescriptions or interventions.

ML/DL is great at finding correlations in data, but not real causation, falling into the trap of equating correlation with causation.

Another crucial widespread error is failing to distinguish Ontological/Generic/Deductive/Top-Down Causation vs. Phenomenological Specific/Inductive/ Bottom-Up/Space-Time Causality.

Causality is about a generic causal relation, with a generic set of causes and effects, with the structural invariance to individual objects or systems, space and time. It is as different as ontology vs phenomenology, theoretical science vs. empirical science, as observables, an appearance, action, change and occurrence of any kind vs. the force/the law producing it.

There are an increasing number of research and development of naive, linear specific/inductive/bottom-up/space-time causality in terms of a statistical AI and ML, as sampled below:

• Judea Pearl and Dana Mackenzie’s The Book of Why. The New Science of Cause and Effect
• Introduction to Causality in Machine Learning
• Eight myths about Causality and Structural Equation Models
• Deep learning could reveal why the world works the way it does
• To Build Truly Intelligent Machines, Teach Them Cause and Effect
• Causal Inference in Machine Learning
• Causal Bayesian Networks: A flexible tool to enable fairer machine learning
• Causality in machine learning
• Bayesian Networks and the search for Causality
• The Case for Causal AI
• Causal deep learning teaches AI to ask why

All of them are confined by a critical misunderstanding of causation that "if X causes Y that does not mean that Y causes X". Then the reverse phenomena is the whole disruption of causality and causation, thus enabling all kinds of interactions of all kinds of real world systems and processes: "if X causes Y that DOES mean that Y causes X".

Again, one of the key formal constructs, Bayes' principle, was invented by its author as being about a reversible generic probabilistic causation. And Bayesian causal networks are used only in studies of general causation, operationalized as singular causality.

"Smoking causes lung cancer" makes most valuable causal knowledge, and it is not "Somebody's smoking causes his lung cancer".

Such an unexampled cognitive bias of naive linear, one-sided causation essentially limits our ability to rely on our causal models and theories as well as on our advanced intelligent systems, AI, ML and DL, for deciding complex world's problems, from climate change to human-like AI robots.

The Universal Principle of Real Causality (PRC)

Causality is considered to be a major principle, a universal law or a conditio sine qua nonof human practice, science and technology.

The first principle which human knowledge, as philosophy and science, might formulate is the principle of causality, which still reigns supreme. The essence of standard causality is the one-sided generation and determination of one phenomenon by another.

The standard Causality Principle states that everything necessarily has a cause. The principle indicates the existence of a relationship between two things/events, the cause and the effect, and an order between them: the cause always precedes the effect. The principle of causality has been variously stated in the history of philosophy and science: Every effect has a cause. Every contingent being has a cause. Whatever comes to be has a cause. What is, has sufficient reason for its existing. The Principle of Sufficient Reason states that everything must have a reason, cause, or ground.

Dictionary Definition of linear causality and causation

Causality is the relation of cause and effect. Causality is causal action or agency. Causality is the doctrine or principle of causes.

Causation is the act, process or agency of causing, producing or effecting; the act or agency which produces an effect. Causation is the relation of cause and effect.

“Correlation does not prove causality.”

Synonyms & Antonyms for causality

Synonyms

• actor, agent, author, creator, designer, former, originator; antecedent, action, causality, causation, cause, condition, ground, occasion, occurrence, origin; influence, force, power, precedent, reason, source, spring; principle, purpose, aim...

Antonyms

• aftereffect, aftermath, consequence, consequent, corollary, development, effect, fate, fruit, issue, outcome, outgrowth, product, result, resultant, sequel, sequence, upshot

[Linear] Causality (also referred to as 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.

[Linear] causality concerns relationships where a change in one variable necessarily results in a change in another variable. [Linear] causation is the demonstration of how one variable influences (or the effect of a variable) another variable or other variables. There are three conditions for causality: covariation, temporal precedence, and control for “third variables.” 

True Causality is the interrelation of causes and effects

[Real] Causality or true causation, or a general cause-effect relationship is an interaction (mutual or reciprocal or circular process, action or influence) by which one general change (a class of events, actions, processes as a cause) produces another general change (a class of events, actions, or processes as an effect) where the cause is productive of the effect, and the effect is inversely productive of the cause.

What is generally missing is the basic truth: Real Causality is the Mother Law, the Law of Laws, the Principle of Principles, or only universal law, being symmetrical, reversible, invertible, convertible and interactive, reversing in effect, action, influence sequence, direction, position, order, or condition, to interact with its cause.

Real Causality or True Causation is the Principle of Principles, Laws of Laws, as well as the Master AI Algorithm.

The Real Causality Principle states that all phenomena in the world are causally interrelated or interacting.

Nowhere in the world can there be any phenomena that have not been caused by other phenomena, and vice versa.

The Principle of Real Causality (PRC) states that causality is a genetic interrelationship of phenomena through which one thing (the cause) under certain conditions causes something else (the effect), and vice versa.

Or, all causal relations are inverted, and all causal laws are symmetric. The mechanism of real causality is associated with the integrated transference of matter, energy and information, as cyberspace. a) physical infrastructures and telecommunications devices; b) computer systems (see point a) and the related (sometimes embedded) software that guarantee the domain's basic operational functioning and connectivity; c) networks between computer systems; d) networks of networks that connect computer systems (the distinction between networks and networks of networks is mainly organizational); e) the access nodes of users and intermediaries routing nodes; f) constituent data (or resident data).

Again, real causality is about a generic causal relation, with a generic set of causes and effects, with the structural invariance to individual objects or systems, space and time.

And any association or correlation or interrelationship implies causality; correlation is an essential component of causality, as a base to determine all causality, linear and nonlinear. Real life and science and technology is all about cause-and-effect relationships, their natural laws, mechanisms, systems and processes, following the Master Algorithm of Science and Intelligence, human and artificial:

Real Causation/Universal Causality (Causal World/Causal Knowledge/Science & Technology) =

Interrelationship (Correlation, Associations, Laws, Rules, Algorithms, Structural Models, Causal Model of the World, Bayesian causal networks, World's Data Patterns, Theoretical Science, Computational Modeling and Simulation) + Observation (Empirical science, Statistics) + Manipulation or Intervention [Experimental science, Experimentation: Hypothesis testing, H0 (null hypothesis) or H1 (primary hypothesis), or A/B/n experiments]

A causal relationship to exist between X and Y IFF there is a bivariate association between X and Y.

The Master Algorithm of Causal Discovery is Descriptive, Deductive, Intuitive, Inductive, Exploratory, Explainable, Predictive and Prescriptive (DDIIEEPP), all to observe the world, compute the world, interact with the world and discover how that world works.

It underpins the scientific method of inquiry depending on the reciprocal interactions of phenomena observations [questions] and generalizations, making conjectures (hypotheses) by inductions and testing hypotheses by conducting experiments or studies, deriving predictions as logical consequences from them by deductions, and then carrying out experiments or empirical observations based on those predictions. Such principles of the scientific method, testing whether the real world behaves as predicted by the hypothesis by an experiment, observational study, field study, or simulation, underlie the development of science since at least the 17th century. 

Again, the Master Algorithm covers five contemporary machine-learning paradigms—evolutionary algorithms, connectionism and neural networks, symbolism, Bayes networks, and analogical reasoning—unified in one “master algorithm” potentially capable of learning nearly anything.

Domingos' envision of machine learning methodologies into a “master algorithm.” The Master Algorithm, Pedro Domingos.

In physics, the principle of causal interactionism is operationalized as the fundamental interactions (fundamental forces) that form the basis of all known interactions in nature: gravitational, electromagnetic, strong nuclear, and weak nuclear forces, allowing a fifth force as well to explain various anomalous observations, like dark matter and dark energy, that do not fit existing theories.

Production or generation, determination, symmetry or covariance, reflexivity and transitivity are the postulates of Real Causality and True Causation.

The Mass-Energy-Information Trinity conservation might come from the universal causal symmetry defining a symmetry (or covariance) of the fundamental laws of nature/physics and the equivalence between energy and mass.

The Ladder of Reality and Causality and AI

If Causal AI is to evaluate whether or not some X causes some Y, it needs to launch 5-step causal algorithms:

(1) what classes, kinds or types of observables or causal data or variables or changes are involved, independent, dependent, confounding, controlled, etc.?

(2) is there covariation/correlation/link between X and Y?

(3) Is there a control for all confounding variables Z that might make the relationship between X and Y a spurious false association or statistical dependency [the Common Cause Principle, the researchers are commonly “failling to control for” some potentially important cause of the dependent variable]?

(4) what is first observed, if X causes Y? Or if Y causes X? Or which is a reverse causation? Or what is a causal effect?

(4) determine causation, test for causation, direct and reversed, find a correlation, anf test for causation by running experimentation “controlling the other variables and measure the difference or causal effect”: Causation = Correlation + Observation + Manipulation or Intervention [experimentation: Hypothesis testing, H0 (null hypothesis) or H1 (primary hypothesis), or A/B/n experiments]

(5) what is a comprehensive nonlinear circular causal model/mechanism/process/path/channel that interrelates X with Y [the “how” and “why” questions]? In the context of AI, it is causal perception-action loops, the cornerstone of autonomous systems such as self-driving vehicles. 

The same process can be applied to a wide variety of causal claims and questions of different complexity. Does drinking red or white wine cause a reduction or increase in heart disease? Does psychotherapy help people with emotional and relational problems? Do increases in government spending boost or retard economic growth? Does democratization cause economic development? Do parental effects (Z) make the relationship between pre-school experience and later educational or learning outcomes spurious? What are the “causes” of historical and social events (the French Revolution, or World War I, the Great Depression, World War I, the Cold War, the COVID-19)?

The Causal AI is analyze the public data sets taking the following programming steps:

 Finding appropriate empirical evidence to evaluate the degree to which a causal claim or theory is or is not supported.

Developing a causal theory about the relationship between an independent variable (X) and a dependent variable (Y) from the data set.

Identifying the credible causal mechanism that connects X to Y.

Explaining its answers: (b) Could Y cause X? (c) What other variables (Z) would you like to control for in your tests of this theory?

In each of these and many other examples, we need to observe a correlation between variables before concluding that the relationship is causal, like as "if economic development causes democratization, then democratization causes economic development".

Spotting, identifying and measuring causal claims make an essential general learning skill, with a causal decision-tree scorecard as a shorthand for summarizing the answers to these 5 questions in square brackets [Yes/No, Yes/No, Yes/No, Yes/No, Yes/No].

In all, the real causation paradigm organizes all possible relations, from dependence, association, correlation, or statistic relationship to a naive, linear causation to a circular nonlinear causality, as the AA ladder of CausalWorld:

• Chance, statistic associations, causation as a statistic correlation between cause and effect, correlations (random processes, variables, stochastic errors), random data patterns, observations, Hume's observation of regularities, Karl Pearson's causes have no place in science, Russell's “the law of causality” a “relic of a bygone age” /Observational Big Data Science/Statistics Physics/Statistic AI/ML/DL [”The End of Theory: The Data Deluge Makes the Scientific Method Obsolete”]
• Common-effect relationships, bias (systematic error, as sharing a common effect, collider)/Statistics, Empirical Sciences
• Common-cause relationships, confounding (a common cause, confounder)/Statistics, Empirical Sciences
• Causal links, chains, causal nexus of causes and effects (material, formal, efficient and final causes; probabilistic causality, P(E|C) > P(E|not-C), doing and interventions, counterfactual conditionals,"if the first object had not been, the second had never existed", If-Then Symbolic AI rules, linear, chain, probabilistic or regression causality)/Experimental Science/Causal AI
• Reverse, reactive, reaction, reflexive, retroactive, reactionary, responsive, retrospective, invert, inverted, inverse or backward process causality, as reversed or returned action, contrary action or reversed effects due to a stimulus, reflex action, inverse probabilistic causality, as "backward propagation of errors" algorithm to train ANNs; P(C|E) > P(C|not-E), as in social, biological, chemical, physiological, psychological and physical processes/Experimental Science/Reverse Engineering
• Interaction, real causality, interactive causation, top-down and bottom-up causation, causal quantum entanglement, self-caused cause, causa sui, causal interactions: true, reciprocal, circular, reinforcing, cyclical, cybernetic, feedback, nonlinear deep-level causality, universal causal networks, as embedded in social, biological, chemical, physiological, psychological and physical processes/Real Science/Real AI/Real World/the level of deep philosophy, scientific discovery, and technological innovation

The Six Layer Causal Hierarchy defines the Ladder of Reality, Causality and Mentality, Science and Technology, Human Intelligence and Non-Human Intelligence (AI).

The CausalWorld [levels of causation] is a basis for all real world constructs, as power, force and interactions, agents and substances, states and conditions and situations, events, actions and changes, processes and relations; causality and causation, causal models, causal systems, causal processes, causal mechanisms, causal patterns, causal data or information, causal codes, programs, algorithms, causal analysis, causal reasoning, causal inference, or causal graphs (path diagrams, causal Bayesian networks or DAGs). 

It reviews a causal graph analysis having a critical importance in data science and data-generated processes, medical and social research and public policy evaluation, statistics, econometrics, epidemiology, genetics and related disciplines.

The CausalWorld model covers Pearl's statistic linear causal metamodel, as the ladder of causation: Association (seeing/observing), entails the sensing of regularities or patterns in the input data, expressed as correlations; Intervention (doing), predicts the effects of deliberate actions, expressed as causal relationships; Counterfactuals, involves constructing a theory of (part of) the world that explains why specific actions have specific effects and what happens in the absence of such actions.

It must be noted that any causal inference statistics or AI models relying on "the ladder of causality" [The Book of Why: The New Science of Cause and Effect] are fundamentally defective for missing the key levels of real nonlinear causality of the Six Layer Causal Hierarchy.

Leibniz's Superscientist and Laplace's Demon as a Causal AI

Gottfried Leibniz (1646-1716), a giant in mathematics, logic and philosophy, imagined a scientist who could see the events of all times, just as all times are thought to be present to the mind of God.

"Everything proceeds mathematically...if someone could have sufficient insight into the inner parts of things, and in addition had remembrance and intelligence enough to consider all the circumstances and take them into account, he would be a prophet and see the future in the present as in a mirror."

In the introduction to his 1814 Essai philosophique sur les probabilités, Pierre Simon Laplace, a giant in mathematics, physics, and astronomy, extended an idea of Leibniz which became famous as Laplace's Demon

"We may regard the present state of the universe as the effect of its past and the cause of its future. An intellect which at a certain moment would know all forces that set nature in motion, and all positions of all items of which nature is composed, if this intellect were also vast enough to submit these data to analysis, it would embrace in a single formula the movements of the greatest bodies of the universe and those of the tiniest atom; for such an intellect nothing would be uncertain and the future just like the past would be present before its eyes."

Laplace postulates a causal super-intelligence that could know the positions, velocities, and forces on all the particles in the universe at one time, and thus know the universe for all times. 

A Laplace Demon has a critical application in the digital age of infinite data. The Data Era is mostly about the world’s digital data, the result of our increasingly digitised way of life.

It is now all about zettabytes of information streaming flows, and how to intelligently process it for insights, predictions, recommendations and actions.

As such, there are about M = 8 billion of data units, as individuals, and hundreds of data features, F, as biometric, psychological, behavioral, social, economic, political or geographic data items.

A global human population data ocean, H=M x F, could have billions of data records of each unit's attributes,age, gender, race, religion, political party, education, geography, job, income, interest, sentiments, health, etc.

Now, add up here engineered items, billions of roads, buildings, machines, computers, smart devices, weapons, etc., with a view to become digital members of the global zettabyte internet of things.

Now, as the amount of data in the world has increased exponentially, how we are going to govern all of it without global datasphere machine intelligence, its analytic tools, codes, algorithms, programs, applications and robots.

Again, according to market intelligence company IDC’s ‘Data Age 2025’ paper, if the ‘Global Datasphere’ (the total of all data created, captured or replicated) in 2018 reached 18 zettabytes, in 2025 the world’s data will grow to 175 zettabytes.

• Google processes more than 40,000 searches every second, or 3.5 billion searches a day.
• 1.5 billion people are active on Facebook every day. That’s one-fifth of the world’s population.
• Two-thirds of the world’s population now own mobile phones.

Add up Cerebras Wafer Scale Engine 2, the largest data processing chip ever built. The Cerebras WSE-2 powers the revolutionary CS-2 system. 2.6 Trillion transistors and 850,000 AI-optimized, fully programmable cores – all packed onto a single silicon wafer to deliver world-leading AI compute density at unprecedented low latencies.

Data, big data, global data, enormous computers, fast memory and communication bandwidth..., no way forward without the Real AI capable of autonomously transforming the world's data into the world's knowledge.

Again, the Laplace's Demon starts coming in with Bayesian Machine Learning as well as with High Frequency Trading (HFT), involving the biggest hedge funds manipulating $100 trillion. HFT is an automated trading platform that large investment banks, hedge funds, and institutional investors employ using powerful computers to transact a large number of orders at extremely high speeds. The algorithmic trading systems use complex algorithms to analyze the markets to spot emerging trends in mls, mks, or nanoseconds. High-frequency traders earn their money on any imbalance between supply and demand, using arbitrage and speed to their advantage. 

Today, algorithms address many of the problems and decisions that have long been central to the business of trading. What instrument(s) should be invested in or traded? What price should be bid or offered? What order size is optimal? What should be the response to a request for a quotation? What risk will be taken on by facilitating a trade? How does that risk change with the size of the trade? Is the risk of a trade appropriate to a firm’s available capital? What is the relationship between the price of different but related securities or financial products? To what market should an order be sent? Is it more effective to provide liquidity or demand liquidity? Should an order be displayed or non-displayed? To which broker should an order be sent? When should an order be submitted to a trading center? In general, algorithms utilize a rich array of market information to very quickly assess the state of the market, to determine when, where, and how to trade, and then to implement the resulting trading decision(s) in the marketplace. 

Hedge funds over the past ten years have evolved from going after the time dimension (HFT) to working in the data dimension (a Laplace Demon).

A Laplace Demon can predict anything as it knows everything instantly and understand their causality links. 

AI is powering a dramatic change in every part of human life, in every society, economy and industry across the globe, emerging as a strategic general purpose technology.

Then it is most significant to know what real and true intelligence is, with all its major kinds, technologies, systems and applications, and how it is all interrelated with reality and causality, and superintelligence, as condensed in the Leibniz's Superscientist and Laplace Demon concept.

What is the Laplace's Demon/Superintelligence?

The Laplace's Demon/Superintelligence is dealing with the universe or reality in terms of the causal world models and data/information/knowledge representations for cognition and reasoning, understanding and learning, problem-solving, predictions and decision-making, and interacting with the environment.

The Laplace's Demon is a man-machine superintelligence or supermind which is modeling and simulating the world to effectively and sustainably interact with any environments, physical, natural, mental, social, digital or virtual. This is a common definition covering any intelligent systems of any complexity, human, machine, or alien intelligences.

Its heart and soul consists in the consistent, coherent and comprehensive causal model of the world.