How Causal Revolution is shaking up Science and Technology
The world is flooded in uncertainty, and only very few things are truly predictable.
This is a time of unparalleled disruption and complexity. Risks—such as pandemics, climate change, inequality and geopolitical tensions—transcend borders and sectors. Despite its apparent chaos, the world is deeply ordered by causal principles, rules and laws helping humanity to discover rational ways to survive and thrive.
Today's science and engineering is unable to face reality, with its scale and scope and complexity of global risks.
We need a new scientific and technological revolution to navigate today’s global risks landscape, to navigate economic and societal gaps to restore social cohesion, boost employment and thrive.
While the scientific revolution represented a change in the human understanding of the world, the new revolution today represents a change in how the world works, what causal mechanisms govern all reality, meta-physical, physical, mental, social, informational, digital or virtual.
Classically, nature or physical reality has been considered to be a linear causal system, where the output depends on past and current inputs but not future inputs, like in any black box systems.
In reality, reality is a giant nonlinear causal system, governed by fundamental causal interactions, causal mechanisms, principles, laws and effects.
Again, the world, with all its structures, is a complex multi-level construction with the upward-downward causation between macro-micro levels, interrelated by feedback mechanisms and emergent phenomena.
The essence of New Causal, Scientific and Technological Revolution is a true, comprehensive, coherent and consistent mapping, modeling, simulating or "understanding" of reality and its causality, mentality, humanity and digitality.
WHAT'S WRONG WITH the SCIENTIFIC METHODOLOGY?
The standard steps or principles of the scientific method include the following algorithm:
It is the iterative inductive-deductive interactions of DATA and THEORIES, data and facts or observations and experimental results with concepts, abstractions or generalizations, hypotheses and theories.
Traditionally, scientific investigation is about the research methods and techniques to develop hypotheses, gather data, conduct experiments, analyze data, and draw conclusions.
The AI project cycle is following the scientific methodology in its basic steps
In reality, any deep research is deep data analytics and causal patterns, laws or interrelationships, causal claims, causal discovery, causal models and causal inferences.
This is REAL SCIENCE, not today's science relying on functional relations and statistical correlations. It is the iterative inductive-abductive-deductive interactions of DATA and CAUSAL MODELS, data and facts or observations and experimental results with REALITY AND CAUSALITY, its concepts, abstractions or generalizations, hypotheses and theories.
CAUSAL REVOLUTION: REAL CAUSATION AS THE ENGINE OF REALITY
By tradition, the "Scientific Revolution" refers to drastic changes in thought & belief, to changes in social & institutional organization, that unfolded in Europe between roughly 1550-1700; beginning with Nicholas Copernicus (On the Revolutions of the Heavenly Spheres), who asserted a heliocentric (sun-centered) cosmos, and added with Isaac Newton (Philosophiæ Naturalis Principia Mathematica), who proposed universal laws and a Mechanical Universe as a new cosmology. It replaced the Greek view of nature that had dominated science for almost 2,000 years.
Since that all science was divided as experimental and empirical science and formal speculative or theoretical non-science.
Everything is learned either bottom-up from data, observations and experiences, or everything is learned top-down from rules, templates, basic facts or ground truths.
The first paradigm, it is what about today's empirical science and its applications, as ML technology, fed by individual observations or experiences, which are merely instantiations of classes and rules or some ontological templates and scientific abstractions, be it naive physics facts or general rules of the commonsense world.
This is what grounds an experimental science unable to learn true facts top-down, but only learning bottom-up, from instantiations to general templates.
The second paradigm is what qualified as a non-science or speculative theoretical science. In fact, any real comprehensive learning of basic facts is impossible without generalizations, and high-level concepts and symbolic representations.
Empiric scientific research is predominantly focused on statistical correlations and functional relations instead of real, causal relationships, with causal discovery, model, reasoning, inference.
The key for any powerful intelligence, human, machine, or alien intelligence is true mapping, modeling, simulating or "understanding" of reality and causality, causation or cause and effect, to effectively and sustainably interact with the world.
In reality, hypothesis-driven research should assume a causal structure, a set of causal relationships among inputs and outcomes, and researchers estimate the effect size of these relationships (e.g. causal inference), as structural equation models (SEM). In such research, drawing a causal conclusion is valid, because prior knowledge ascertains that the relationships are indeed causal (rather than merely associative or correlative). And when there is no knowledge of the causality, the causal structure itself needs to be discovered from data through a process known as causal structure discovery.
Causal structure is the set of causal relationships among a set of variables, and causal structure discovery (CSD) is about learning the causal structure from a stream of real-world data, observational, experimental or computational.
CSD is about identifying causal relationships from large quantities of data through computational methods.
Instead of traditional association-based ML computational methods to discover data patterns, (i) CSD methods can discover the unknown causal relationships from observational data and (ii) to offer guidance to accurately discover unknown causal relationships.
As the “gold standard” graph serves a fully connected undirected causal graph, with all edges connecting conditionally all variables, dependent or independent variables.
Causality or Causation should be modelled as an interrelationship between input variables X and output variables Y such that changes in X lead to changes in Y, and vice versa.
The universal abstraction or complex nonlinear causality is supported by a lot of facts and evidences and rules, as in every domains or fields of knowledge and practice:
complex behaviors,
interactions,
interfaces,
circular causality,
reciprocity, mutuality,
nonlinear phenomena in physics, biology, and social sciences,
chemical reactions,
cybernetics, self-regulating mechanisms, feedback loops between processes or inputs and outputs, qualitative or quantitative, positive/negative with self-reinforcing/self-correcting, reinforcing/balancing, discrepancy-enhancing/discrepancy-reducing or regenerative/degenerative,
biofeedback,
social feedback,
information feedback,
nonlinear narrative in literature and cinematography,
the special backpropagation algorithms for training neural networks, reinforcement learning,
and Bayes' theorem (Bayes' law or Bayes' rule; Bayes–Price theorem)
True causality is fundamentally symmetric, i.e., causes lead to effects and the other way around.
CAUSALITY as the INTERRELATIONSHIP of Cause and Effect
Humans fail to identify causality, being widely misconceived that it is all about. Many see causation as either non-existent or mere some associations and correlations or some causal nexus, linear connections or some interventions and counterfactual thinking or grammatical constructions.
Many have no idea what kind of entity can be a cause, and what kind of entity can be an effect, and that every cause and every effect is respectively some CHANGE, be it process, event, becoming, or happening, or VARIABLE, but hardly any substance, object, entity or state of affairs.
Causality and Causation are falling under the biggest ever misunderstanding and most harmful ever misconceptions.
Here is a typical passage of misinterpretation of deeply biased minds.
“Causation is not an actual physical process or law or principle, it is a mere linguistic term , denoting a myriad of different physical events, it is a shorthand, a placeholder, a purely grammatical convenience . Eg, we say, heat causes water to boil, a acorn causes an oak tree, gravity causes water to run downhill, a punch causes pain , love causes heartache, etc. ad infinitum. In each instance what is denoted by the linguistic token "cause" are totally unrelated different events and processes”.
The wiki article gives another messy definition: “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”.
It all starts from the wrong assumption which we all are used to think or believe, causality/causation is the bonding or link between a cause and its effect.
The reality of causality and the mechanism of causation are completely different than we all used to think.
THINGS CAUSE THINGS.
ALL THINGS HAVE CAUSES.
ALL CAUSES HAVE EFFECTS, AND VICE VERSA.
NO CAUSATION, NO CHANGE.
NO CHANGE, NO WORLD
They all say: “Causality is the relation between cause and effect”.
That’s badly wrong!
CAUSALITY as the INTERRELATIONSHIP of Cause and Effect
REAL CAUSALITY IS ABOUT REVERSIBILITY AND CIRCULARITY, MUTUALITY AND RECIPROCITY, INTERDEPENDENCE AND CORRELATION.
REAL CAUSALITY IS ABOUT INTERACTION, FORCE, AGENCY,EFFICIENCY, GENERATION, PRODUCTION, INFLUENCE and EMERGENCY.
REAL CAUSALITY IS ABOUT SIMULTANEOUS NECESSITY AND SUFFICIENCY.
Real and true and genuine CAUSALITY is the INTERRELATIONSHIP of cause and effect, causality interrelates or is interrelated; interacts or is interacted.
CAUSALITY IS THE INTERRACTION OF CAUSE AND EFFECT, when an action is influenced by other actions as reactions.
CAUSATION IS AN INTERACTION, when two or more entities have an effect upon one another.
CAUSALITY IS A NONLINEAR RELATIONSHIPS, where cause and effect flow in all possible directions between or among two or more parts, elements or systems. Its feature is a feedback that an effect reacts to a cause. Nonlinear causality can lead to self-reinforcing or self-amplifying processes through its feedback mechanism, enabling a huge disproportionality between initial cause and final effect, the butterfly effect.
Nonlinear causality is the bidirectional flow of causation between the macro and micro levels within a system, thus enabling downward causation.
Nonlinear causality implies reverse/converse/inverse/inverted/transpose/backwards/contrary/opposite causality in time or order; that set future goals can feedback to affect current events, first described by Aristotle as a final cause.
Nonlinear causation implies the action of many variables in creating a cause or vise verse. A single confounding cause can have many effects, such as a general theory of electromagnetism and all its applications, or gravity and its effects, or inversely many causes can have a single effect such as a weather being the product of temperature, pressure, wind, humidity, etc.
Accordingly, there are a bottom-up causality ALWAYS completed with the top-down causation, and vice verse.
Whereas linear reductionist causality implies that causality goes, runs or flows from the bottom-up, but not in the reverse direction, nonlinear causality implies both upwardly caused and downwardly caused processes, with causation flowing bidirectionally from the micro to the macro and backwards.
So, in the Downward-Upward Nonlinear Causation, the “cause” is coming from the environment to affect the state of its individual elements.
Thus the overall structure of the system is affecting the specific phenomenon in a downward way. The low-level interactions affect the whole, creating a nonlinear causal relationship between the system’s micro and macro level with a causal feedback, negative or positive. Both top-down causality and bottom-up causation can occur at the same time.
In all, there are several paradigms or patterns to map out the NC connections:
NC allows to identify causes, as primary and main or contributary or occasional, if they belong to any of three categories:
(1) necessary causes, which must be present for a change to occur but may not be solely responsible for the event, it is impossible to have C without E
(2) sufficient causes, which are all that is needed to cause a change.
(3) contributory causes, which help bring about changes but can't produce effects independently.
CAUSALITY IS A NECESSARY AND SUFFICIENT RELATIONSHIP OF CAUSES AND EFFECTS.
C ↔⇔≡⟺E; C "if and only if" E include: C is necessary and sufficient for E; C is necessary for E and C is sufficient E, C > E.
All real causal statements are necessary and sufficient statements, where the two statements must be either simultaneously true, or simultaneously false.
There are many critical reasoning errors and logical fallacies or cognitive biases, associated with causal relationships. A few of the more common ones: (1) ignoring multiple causes, (2) mistaking chronology for causation, post hoc, ergo propter hoc, "after this, therefore because of this", (3) confusing causes with effects, (4) ignoring backwards effects, (5) mistaking reverse causality as an error.
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Crucial, in contrast to linear causality that creates a deterministic model of the world, nonlinear causality embraces both stochastic or probabilistic or indeterministic and deterministic paradigms.
Causative interaction or Interactive Causation or Interaction is the key mechanism of the world and the key construct of all sciences and technologies, physics, chemistry, biology, cognitive science, sociology, economy, or computer science and engineering.
Causative interaction or Interactive Causation or Interaction is the key mechanism of the world and the key construct of all sciences and technologies, physics, chemistry, biology, cognitive science, sociology, economy, or computer science and engineering.
In physics, there are four known fundamental interactions: the electromagnetic, strong, weak and gravitational interactions. And a theory of everything is where all its fundamental interactions are unified. It is hypothesized that the combinations of many simple interactions can lead to emergent phenomena, as complex systems or integrative levels of organization, the strata of layers. It structures reality, arranging all entities, structures, and processes in the universe, or in a certain domain, into a hierarchy, like matter, life, mind, society and machine superintelligence,
In all, it implies all kinds of interrelationships, as reciprocal or mutual relations: connection, connect, interaction, interconnection, interdependence, interdependencies, interlink, interplay, inter-dependencies, correlation, linkage, inter-relation, interrelatedness, or pattern and order.
True causality is an integrated relation and interactive relationship. It is nonlinear by the very nature, where
Cause Causes Effect if and only if Effect Causes Cause, or X causes Y iff Y causes X
That means: a variable, X, can be said to cause another variable Y, if when all confounders are adjusted, an intervention in X results in a change in Y, and an intervention in Y does necessarily result in a change in X. This is in line with correlations, which are inherently symmetric, i.e., if X correlates with Y, Y correlates with X, as if X causes Y, Y MUST cause X.
Bayesian and causal networks are completely identical. And the difference lies in their misinterpretations. Consider the simple example in the figures below.
Example network that can be interpreted as both Bayesian and causal. Fire and smoke example adopted from Pearl [The Book of Why: The New Science of Cause and Effect].
Here we have a network with 2 nodes (fire icon and smoke icon) and 1 edge (arrow pointing from fire to smoke). This network can be both a Bayesian or causal network.
The key distinction, however, is when interpreting this network. For a Bayesian network, we view the nodes as variables and the arrow as a conditional probability, namely the probability of smoke given information about fire. When interpreting this as a causal network, we still view nodes as variables, however the arrow indicates a causal connection. In this case both interpretations are valid.
Now, if we were to revert the edge direction, the causal network interpretation would be still valid, regardless that smoke does not cause fire.
Fire is the rapid oxidation of a material (the fuel) in the exothermic chemical process of combustion, or burning, releasing heat, light, and various reaction products, including gaseous by-products, as smoke. It is commonly an unwanted by-product of fires (including stoves, candles, internal combustion engines, oil lamps, and fireplaces). Smoke is not included in the fire tetrahedron, oxygen, heat, fuel and chemical reaction. Take any of these four things away, and you will not have a fire or the fire will be extinguished.
Fire causes heat and light as much as light or heat could cause fire. Again, if the Fire is a rapid oxidation of a material, their must be an inverse process of deoxidation/reduction which occurs when there is a gain of electrons or the oxidation state of an atom, molecule, or ion decreases. Or, as to the inverse causality law, if one chemical species is undergoing oxidation then another species undergoes reduction.
So, X causes Y if an intervention in X results in a change in Y, and an intervention in Y does necessarily result in a change in X.
That all means that we have one of biggest human misconceptions, cognitive biases or informal fallacies, misguiding human decision-making and practice for all the human history.
Again, a statistical input-output dependence or asymmetric functional relationships of dependent and independent variables as used in mathematical modeling, statistical modeling and experimental sciences should be reviewed.
Depending on the context, an independent or exogenous variable is called a "predictor variable", regressor, covariate, "control variable" (econometrics), "manipulated variable", "explanatory variable", exposure variable, "risk factor" (medical statistics), "feature" (in machine learning and pattern recognition) or "input variable".
A dependent endogenous variable is called a "response variable", "regressand", "criterion", "predicted variable", "measured variable", "explained variable", "experimental variable", "responding variable", "outcome variable", "output variable", "target" or "label".
Causal relationships between variables in real-world settings are much more complex and may consist of direct and indirect effects, that go directly from one variable to another or when the relationship between two variables is mediated by one or more variables.
From the Black Box Paradigm to the White Box Systems
In science, computing, and engineering, mathematics and statistics, a black/opaque box technique is domineering. The BB system is viewed in terms of its inputs and outputs (or transfer characteristics/function), without any knowledge of its internal workings. A transfer function (system function or network function) of a system, sub-system, or component is a mathematical function modelling or mapping out the system's output for each possible input. This function could be a two-dimensional graph of an independent scalar input versus the dependent scalar output, called a transfer curve or characteristic curve.
The understanding of a black box is based on the defective hypothesis of a linear causal relation between the input and the output. This principle states that input and output are distinct, that the system has observable (and relatable) inputs and outputs and that the system is black to the observer (non-openable).
This makes the basis of the open flow system, having input and output flows, representing exchanges of matter, energy or information with its surroundings. Its interactions with the environment can take the form of information, energy, or material transfers into or out of the system boundary, depending on the discipline defining the concept, widely applied in the natural and social sciences, computer science and engineering sciences.
The BB systems could be transistors in electronics, control systems, engines, algorithms, ML/DL/AI systems, the human brain, an institution, government, etc.
Now, with the causal network graph innovated as a transfer/system/network function, the whole BB paradigm is replaced by the the White Box (or glass box, clear box, or open box) paradigm, where the inner components, mechanisms or logic are available for inspection and could be properly altered.
A so- called XAI, explainable AI, to be trustworthy and transparent, interpretable and explainable, responsible and robust must be real-world nonlinear causal systems with causal networks data graphs, where the output depends on past, current inputs and future inputs.
HAI systems avoid “one-size-fits-all” requirements in terms of explainability, and the industry is actively exploring technical solutions for explainable AI. In the industry practice, an interpretable AI is introduced by the Google Model Cards mechanism, IBM's AI Fact Sheets mechanism, Microsoft's datasheets for datasets mechanism, and other interpretability tools, like Tencent's "matryoshka-style" AI algorithms. Explainability and Interpretability have to do with how true a ML model can identify causaliti in the data, associating a cause to an effect.
Establishing delineated causal relationships is a key method of empirical research in natural and social sciences, manipulating the independent variable in order to determine the impact on a dependent variable in the control settings (laboratories).In fact, specific causal links from one variable, X, to another, Y, cannot usually be assessed from the observed association between the two variables. The reason is that a big part of the observed association between two variables may arise by reverse causation (the effect of Y on X) or by the confounding effect of a third variable, X, on Z and Y.
"Consider, for example, a central question in education research: “Does class size affect test scores of primary school students? If so, by how much?” A researcher may be tempted to address this question by comparing test scores between primary school students in large and small classes. Small classes, however, may prevail in wealthy districts, which may have, on average, higher endowments of other educational inputs (highly qualified teachers, more computers per student, etc.) If other educational inputs have a positive effect on test scores, the researcher may observe a positive association between small classes and higher test scores, even if small classes do not have any direct effect on students' scores. As a result, observed association between class size and average test scores should not be interpreted as evidence of effectiveness of small classes improving students' scores.
That's biggest, badly entrenched misconception; for "causation implies correlation", and vice versa, association implies causation.
Universal Causal Network
Causality is represented mathematically via Structural Causal Models (SCMs), with two key elements, a graph and a set of equations.
The golden standard causal graph, C = <E, R>, is a Bidirected Cyclic Multi-Graph or Causal Loop-Graph Network (BCG/CGN), where entity-vertices E (circles, nodes, points) in a causal BCG represent variables and edges R (arrows, links, ties, arcs, lines) represent causation, direct or inverse.
Again, the BCG/CGN embraces all Cause Analysis Tools, from the fishbone and scatter diagrams, Pareto chart to as interrelationship diagraphs, relations diagrams or digraphs, network diagrams, or data matrix diagrams, defined as a new management planning tool that depicts the relationship among factors in a complex situation showing cause-and-effect relationships.
Fishbone Diagram Example
The Causal Network Graphs as a data matrix diagram is used for analyzing and displaying the relationship between data sets, showing the relationship between two, three, or four groups of information and giving information about the relationship, such as its strength, of the roles played by various individuals or measurements. Six differently shaped matrices are possible: L, T, Y, X, C, and roof-shaped (X<> X when also X<> Y in L or T), depending on how many groups must be compared.
Covering all possible interdependencies, the Universal Causal Network (UCN) is the guiding reference to all sorts and kinds of networks, as Bayes Net, Markov networks, directed and acyclic or undirected and cyclic graphical models, and neural networks, biological or artificial.
It is applied in the root cause analysis for a problem or situation to understand links between ideas or cause-and-effect relationships, how different aspects of the problem are connected; to see relationships between the problem and its possible causes that can be further analyzed.
The Causal Network Graphs are strongly connected containing a directed path from x to y (and from y to x) for every pair of vertices (x, y), while having circuits or loops, that is, arcs that directly connect nodes with themselves, and multiple arrows with the same source and target nodes, thus covering all possible directed graphs as a Directed Acyclic Graphs (DAG) or weighted directed graphs/networks .
The set of equations is a Structural Equation Model (SEM), but showing the causal interconnections and the details of the relationship. SEMs represent all possible interrelationships between or among variables. These equations are symmetric meaning equality works in two directions. This has the implication that SEMs can be inverted to derive alternative SEM equations.
If the Real AI models the relationship between a disease and the symptoms it produces, Y = mX + b, it also accounts for an inverted relationships between symptoms and diseases, X = (Y - b)/m. Or, if diseases cause symptoms, then we have to interpret the second equation as the symptoms cause diseases!
In the case of modeling causation as a linear relationship between two variables X and Y such that changes in X lead to changes in Y, we have deeply defective causal models, reasoning, inference and causal discovery algorithms. [Pearl, J. Causality: Models, Reasoning, and Inference. Vol. 64 (Cambridge University Press, 2000)].
Illustration by Julia Suits, author of The Extraordinary Catalog of Peculiar Inventions, and The New Yorker cartoonist
In other words, The Book of Why: The New Science of Cause and Effect by Judea Pearl, basing on a deficient asymmetric causality and DAG/SEM, causal inference and causal discovery, is resulting in scientific malpractice and flawed methodology for statistics, epidemiology, social sciences, eco\nomics, political sciences, computer science, AI and ML.
No Cause, No Change, and No World
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 much advanced since Aristotle's ideas of four causes, especially in terms of complexity and nonlinearity. What we have now, it is more formalized and much narrower simplistic statistic 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 universe 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 existent and predictable.
Conclusion
Science and Technology is all about a true, comprehensive, coherent and consistent mapping, modeling, simulating or "understanding" of reality and its causality, causation or cause and effect.
Resources
SUPPLEMENT 1
The Book of Why: The New Science of Cause and Effect Hardcover – May 15, 2018
The book has a false assumption, "Correlation is not causation", badly misconceiving the great idea of causality, causation or cause and effect, corrupting many good minds as one could read below.
Editorial Reviews
Review
One of Science Friday's "Best Science Books of 2018"
"Illuminating... The Professor Pearl who emerges from the pages of The Book of Why brims with the joy of discovery and pride in his students and colleagues... [it] not only delivers a valuable lesson on the history of ideas but provides the conceptual tools needed to judge just what big data can and cannot deliver."―New York Times
"Cause and effect is one of the most heavily debated, difficult-to-prove things in science and medicine. This book really gets you thinking about cause and effect as it applies to issues of our time, such as: How come cigarettes were around for years and we never showed they were causing cancer or heart disease? The authors goes through these cases like an interrogation, and it's just extraordinary."―Science Friday
"Seriously, everyone should read The Book of Why."―Jeff Witmer, American Mathematical Monthly
"'Correlation is not causation.' That scientific refrain has had social consequences...Judea Pearl proposes a radical mathematical solution...now bearing fruit in biology, medicine, social science and AI."―Nature
"Lively and accessible...Pearl was one of the visionary leaders of the causal revolution, and The Book of Why is his crowning achievement."―Jewish Journal
"Anyone interested in probing connections between cause and effect, and their relevance for the future of AI, will find this a fascinating and provocative book. Highly recommended."―CHOICE
"Judea Pearl is on a mission to change the way we interpret data. An eminent professor of computer science, Pearl has documented his research and opinions in scholarly books and papers. ... With the release of this historically grounded and thought-provoking book, Pearl leaps from the ivory tower into the real world...Pearl has given us an elegant, powerful, controversial theory of causality."―American Mathematical Society
"Have you ever wondered about the puzzles of correlation and causation? This wonderful book has illuminating answers and it is fun to read."―Daniel Kahneman, winner of the Nobel Memorial Prize in Economic Sciences and author of Thinking, Fast and Slow
"Pearl's accomplishments over the last 30 years have provided the theoretical basis for progress in artificial intelligence... and they have redefined the term 'thinking machine.'"―Vint Cerf, Chief Internet Evangelist, Google, Inc.
"Judea Pearl has been the heart and soul of a revolution in artificial intelligence and in computer science more broadly."―Eric Horvitz, Technical Fellow and Director, Microsoft Research Labs
"If causation is not correlation, then what is it? Thanks to Judea Pearl's epoch-making research, we now have a precise answer to this question. If you want to understand how the world works, this engrossing and delightful book is the place to start."―Pedro Domingos, professor of computer science, University of Washington, and author of The Master Algorithm
"The Book of Why ... questions and redefines the building blocks of our AI systems"―theverge.com
Only a few have noticed its real value: I hate to tell you this, but science, at least real science, has linked cause and effect.
What the entire farce is doing is called obfuscation. If you are so confused by the math, technical jargon, sciency graphs and tables and data and figures, then you just feel dumb and agree with whatever idiotic conclusion the author invents. Look how cutting taxes and increasing federal spending stimulates the economy with all my sciency charts and formulas! It's an entire scam industry and this author is just like another grifter.