Primer: Causality In Models — Brian Romanchuk

Since is about identifying regularity in change and developing theories that explain the causation, ideally in terms of variables and linear functions. This is a challenge even in complicated simple systems, e.g, in the natural sciences, and it is a huge challenge when dealing with complex adaptive systems in the life and social sciences. 

Complex adaptive systems are synergistic, meaning that they are greater than the sum of the parts, so that examining the parts alone is insufficient for analyzing the system as a whole, that is, the parts and their relationships.

Complex adaptive systems are also subject to emergence, that is, the appearance of properties that are unforeseeable based on analysis of the existing system. For example, systems with the capability to learn from feedback and change behavior based on learning are unpredictable based on discovery of new knowledge and its application. There is as yet no logic of discovery that formalized the process and no scientific theory that has penetrated the causality so to be be able to influence it.

Furthermore, a framework for approaching causality must be assumed and ideally defined operationally in science, as Brian does in this first sentence following:

One important consideration for indicator construction is the notion of causality (using systems engineering terminology). A non-causal model is a model where the output depends upon the future values of inputs. In the absence of access to a time machine, such a model cannot be directly implemented in the real world. In practice, a non-causal model output is “revised” as new datapoints are added to input series. The result is that we cannot use the latest values of the series to judge the quality of previous “predictions” of the model.

The use of non-causal model might be acceptable for the analysis of a historical episode, or an earlier economic regime (such as various Gold Standard periods). Since new data will not arrive, there will be no revisions....

However, causation is still an open question in the philosophy of science.•

Bond Economics
Primer: Causality In Models
Brian Romanchuk

• Causality can be defined as the "causal" connection between cause and effect, e.g., in terms of conditionality (sufficient condition, necessary condition, necessary and sufficient condition). Causality is established though a scientific theory that accounts for the connection, since "correlation is not causation."

Causation is the entire scope of the subject, which includes "causality" as just defined but is not limited to it. There are ontological and epistemology issues regarding causation that are not settled. 

Generally speaking, modern science assumes 1) ontological monism in assuming naturalism, that is, that "everything" can be explained by natural causes as observables (as in a theory of everything). It also assumes 2) epistemological realism in the sense that the mind (subjectivity) is the mirror reality (objectivity) when the scientific method is correctly applied. 

However, these assumptions regarding the framework for gaining knowledge are more presuppositions than stated assumptions. Philosophy of science attempts to bring clarity to this by examining the various issues that arise.

Timothy Taylor — Pareidolia: When Correlations are Truly Meaningless

"Pareidolia" refers to the common human practice of looking at random outcomes but trying to impose patterns on them. For example, we all know in the logical part of our brain that there are a roughly a kajillion different variables in the world, and so if we look through the possibilities, we will will have a 100% chance of finding some variables that are highly correlated with each other. These correlations will be a matter of pure chance, and they carry no meaning. But when my own brain, and perhaps yours, sees one of these correlations, I can feel my thoughts start searching for a story to explain what looks to my eyes like a connected pattern.…

Classes in statistics emphasize that "correlation doesn't mean causation." The lesson here is even stronger. Correlation doesn't necessarily mean anything at all.Classes in statistics emphasize that "correlation doesn't mean causation." The lesson here is even stronger. Correlation doesn't necessarily mean anything at all.

Conversable Economist
Pareidolia: When Correlations are Truly Meaningless
Timothy Taylor | Managing editor of the Journal of Economic Perspectives, based at Macalester College in St. Paul, Minnesota

Lars P. Syll — In search of causality

Causality is one of the fundamental problems in philosophy, covering epistemology, philosophy of language, semiotics, and philosophy of science. Since causality is the basis of explanation, it applies to all aspects of understanding and theorizing, as Aristotle pointed out in his Metaphysics millennia ago. Yet, there is still no complete understanding of causality that would end controversy.

There many interrogatives — who, what, when, where, how and why, for example. Description involves the facts — what what, when, where, how much and how long, etc. Explanation involves means and ends — "how" (Greek techné) and "why" (telos).

Natural science deals chiefly with the how. "Speculation" deals with the why. Aristotle opined that all speculation begins with wonder. The Greek word for "speculate" that Aristotle uses is theorein. The root is theo which means god, or divine. Speculation is contemplative rather than active. It involves reflection on experience.

An archaic English term for "to speculate" is "to divine." It means to discern the inner workings. We see the sun rise and set and still speak of the "sunrise" and "sunset," but now we know that the sun is not actually moving at all; the rotational movement of the earth is "causing" the experience.

What we wonder about is a "puzzle" to us. The Greek term is aporia. The root means "impasse." This "causes" us to speculate about how and why in search of an explanation as a "theory."

In ancient time, most of the answers to such foundational questions involved supernatural causes expressed in myths, which were largely anthropomorphisms about natural forces. At the time of the Axial Age, interest shifted toward intellectual (logical) reasoning in place of myth as storytelling became less satisfying intellectually.

Aristotle was the first person in the West to systematize knowledge largely in the form that it has been handed down through the centuries in the West. He understood that a requirement for gaining true knowledge (epistemé) through inquiry was to understand reasoning, so he wrote books on logic as a prerequisite.

Aristotle was also understood that knowledge of the world comes through the senses and so he emphasized the role of observation in gaining knowledge. He was particularly interested in biology as a science understood as theory based on observation rather than storytelling.

Aristotle also recognized the existence of foundational issues that "come before," or are "meta," as we say even today. These are properly the issues for intellectual inquiry, which we still call "philosophy." meaning love of wisdom. Here the Greek term sophia means speculative wisdom rather than practical wisdom. Speculative wisdom is concerned with the way, while practical wisdom is concerned with the how.

Aristotle seems to have gotten off on the wrong foot in some instances, but overall the paradigm of knowledge he set forth still holds sway in the West. In fact, Aristotelianism is now making a comeback.

Today, we are still arguing about causality, what counts as causal explanation, and the degree to such ultimate explanation is possible given bounded rationality.

Lars P. Syll’s Blog
In search of causality
Lars P. Syll | Professor, Malmo University

Lars P. Syll — Truth and probability

Keynes and the fundamentals of probability.

Lars P. Syll’s Blog
Truth and probability
Lars P. Syll | Professor, Malmo University

Mike Steiner — Causes in Real Life – How Organizations Perform a Root Cause Analyses (RCA)

Not a priority but of interest if for those who want to know more about how organizations deal with causation by analyzing the concrete in terms of the abstract. 

This is related to what Hegel called "concrete universal, and Marx defined as "concrete abstraction." This is the basis of the dialect for Hegel and Marx's adoption and adaptation of it.

A Philosopher's Take
Causes in Real Life – How Organizations Perform a Root Cause Analyses (RCA)

Mike Steiner | Strategic Initiative Specialist at TransCanada

Lars P. Syll — The main reason why almost all econometric models are wrong

Since econometrics doesn’t content itself with only making optimal predictions, but also aspires to explain things in terms of causes and effects, econometricians need loads of assumptions — most important of these are additivity and linearity. Important, simply because if they are not true, your model is invalid and descriptively incorrect. And when the model is wrong — well, then it’s wrong....

Simplifying assumptions versus oversimplification.

Lars P. Syll’s Blog
The main reason why almost all econometric models are wrong
Lars P. Syll | Professor, Malmo University

Daniel Little — New thinking about causal mechanisms

Everyone is familiar with the nostrum, "correlation is not causality." Simply put, correlation can potentially identify input-output relationships with a certain degree of probability. But the relationship is a "black box."

Causal explanation involves opening the box and examining the contents. Correlation shows that something happens; causality in science explains how it happens, elucidating transmission in terms of operations. In formal systems the operators are rules, e.g., expressible by mathematical functions.

Generally speaking correlation is probabilistic, whereas causality is deterministic. Causes are logically antecedent to effects, but arguments based on prior occurrence are post hoc ergo propter hoc fallacies.

There is also probabilistic causation.

Informally, A probabilistically causes B if A's occurrence increases the probability of B. This is sometimes interpreted to reflect imperfect knowledge of a deterministic system but other times interpreted to mean that the causal system under study has an inherently indeterministic nature.

Causality in philosophy involves provision of an account of why something happens based on principles.

Causation is at the heart of the fundamental problems in philosophy of science. It's exploration began in the West in earnest with Aristotle and it has become one of the enduring questions.

Understanding Society
New thinking about causal mechanisms
Daniel Little | Chancellor of the University of Michigan-Dearborn, Professor of Philosophy at UM-Dearborn and Professor of Sociology at UM-Ann Arbor

Diane Coyle — Statistics vs truthiness

[Howard Wainer's] Truth or Truthiness a collection of essays in effect, published as a response to this brave new world of truthiness (ie. lies that people believe because they want to) in politics and public debate. Wainer writes very clearly about statistics in general, and his main theme here, causal inference. This is of course dear to the heart of economists, and gratifyingly Wainer recognises that the profession is more scrupulous than most disciplines about causation. The book starts by underlining the importance of having a clear counterfactual in mind and thinking – thinking! – about how it might be possible to estimate the size of any causal effect. As Wainer puts it, “The real world is hopelessly multivariate,” so untangling the causality is never going to happen without careful thought.

I also discovered that one aspect of something that’s bugged me since my thesis days – when I started disaggregating macro data – namely the pitfalls of aggregation, has a name elsewhere in the scholarly forest: “The ecological fallacy, in which apparent structure exists in grouped (eg average) data that disappaears or even reverses on the individual level.” It seems it’s a commonplace in statistics – here’s one clear explanation I found. Actually, I think the aggregation issues are more extensive in economics; for example I once heard Dave Giles do a brilliant lecture on how time aggregation can lead to spurious autocorrelation results....

Any competent logician can explain how it is illogical to proceed necessarily from individual to general owing to the fallacies of composition and hasty generalization, or to proceed from the general to the individual without regard for synergy, that is, the whole is greater than the sum of the parts.

Consequently, assuming methodological individualism and microfoundations is fraught with pitfalls. Getting the causality right is difficult in social science, even in specific cases, as the difficulty in replicating studies shows.

The Enlightened Economist
Statistics vs truthiness
Diane Coyle | freelance economist and a former advisor to the UK Treasury. member of the UK Competition Commission, and acting Chairman of the BBC Trust, the governing body of the British Broadcasting Corporation

Jason Smith — Stock-flow consistency is tangential to stock-flow consistent model claims

Critique of Godey & Lavoie and MMT based on an analogy with electrical circuitry.

As I mentioned above, the results of SFC analysis have little to do with the SFC itself, but instead depend on the assumptions about the behavior of the "circuit elements" (firms, households, government) about which SFC analysis tells us almost nothing. 

SFC is an operational description of financial flow — LHS and RHS must balance just as electrical charge in a circuit, that is, sum to zero. No causality implied.

Similarly, the electrical charge in every operable circuit must sum to zero — positive and negative charges much cancel.

But in every circuit the actual components have a use to perform that his not revealed by the circuit diagram but the use to which the circuit is being put.

This corresponds to the theoretical aspect in economics. Accounting identifies are the boundary conditions and reveal nothing about causality. Causation requires theoretical interpretation.

The point of using SFC models, like drawing circuit diagrams in electrical and electronic systems, is to ensure that the boundary conditions are observed on the analogy that Jason Smith provides in terms of conservation in physics.

But to say that SFC is a truism and dismiss it as irrelevant would be applicable only if there were not violations in attempted applications.

Just as conservation laws are the framework for theories about natural sciences, so to is accounting with respect to finance. Economics uses finance as its basis insofar as it uses monetary units —economic units (actual) are converted to financial expressions in a unit of account. Finance underlies economic in in a monetary production economy. For one thing, it is not possible to do mathematical calculations with different real units — add apples and oranges — but it becomes possible by reducing both to price.

This relationship between economics (actual entities like goods and workers) and finance (nominal and real value expressed in price and wages) is where many of the issues in economics arise. to begin with, nominal value is an observable, while real value depends on calculation based on assumptions.

SFC deals with the financial side of economics expressed in nominal units, the operational basis of which which many economist not only do not pay much attention to but also don't seem to understand very well.

For example, at the macro level conventional economics doesn't take history into consideration, e.g., the result of switching from a gold standard to a fiat monetary regime regarding monetary and fiscal policy. This was a chief MMT criticism of the Reinhart & Rogoff study that was used to argue for the fiscal austerity that undermined demand prolonged the Great Recession. Because the monetary units were the same, R&R assumed that conditions were comparable, which they were not, as the MMT economists pointed out.

Under a fixed convertible system, monetary policy takes precedence of the currency issuer will lose control of the value of the currency. This doesn't apply in a fiat regime, greasy increasing fiscal space. This has nothing to do with individual agents or their behavior since it is a policy choice. However, it has a great deal to do with what individual agents can do based on policy choice consequent on that initial policy choice.

Fixed and floating rate monetary system operation under the same SFC principles, however, they different theoretical in terms of causality. In a fixed rate system, the currency issuer is constrained operationally in a way that it is not in a floating rate system. That is to say, different initial policy financial choices impact non-financial behaviors.

For instance, the ideal of macro as a policy science is to reconcile the trifecta of growth, employment and price stability. This is impossible in a fixed rate system where the currency issuer must protect the value of the currency using the interest rate. However, it becomes possible in a floating rate system where that constraint is removed, as MMT analysis shows based on the government balance being the inverse of the nongovernment balance at full employment using functional finance and an ELR (employer of last resort),with the fiscal authority  operating similarly to the monetary authority (central bank) acting as LLR (lender of last resort).

While an electrical circuit diagram may look the same for different devices, the actual devices and components and what they can do will be different in the case of different power sources and inputs. The laws of electricity that apply are the same across devices, similar to the financial conditions applying to SFC, but the uses are varied and must be approached specifically.

Similarly, a monetary production economy is powered economically by energy, work, and natural resources and financially by money. Insufficient funding results in idle resources and deflationary pressure, while funding in excess of production increase results in inflationary pressure building. SFC models are useful in modeling this.

Perhaps most important for the study of economics, physical laws are applicable always and everywhere — they are timeless. But since economics in monetary production economies is based on finance and finance is based on institutional arrangements that are arbitrary rather than fixed, there are no financial laws that natural rather than positive, hence to economic laws that are natural to the degree that finance (nominal values based on a unit of account) that are natural rather than positive either. Economics is not a natural science or even like a natural science. It is social and historical.

Because economics is social an historical, there can be no timeless laws regarding causation that obviate assessment of changing conditions (context). SFC states no more than that the accounting procedures in force in a jurisdiction must be adhered to.

While these may differ among jurisdictions, there is common agreement that accounts must balance in accounting periods, e.g., for financial institutions that generally means daily. While variations may exist over the day, at the end of day accounts are settled and must balance. Bank tellers don't do home until they do.

Information Transfer Economics
Stock-flow consistency is tangential to stock-flow consistent model claims
Jason Smith

Peter Dorman — The Identity-Equals-Causation Fallacy, Yet Again

Informal fallacy of false cause.

Econospeak
The Identity-Equals-Causation Fallacy, Yet Again
Peter Dorman | Professor of Political Economy, The Evergreen State College

Bill Mitchell— Flow-of-funds and sectoral balances

I have noted some misperceptions about the derivation, meaning and application of the so-called sectoral balances framework that is used in Modern Monetary Theory (MMT) to help explicate the relationship between the government and the non-government sectors. Some of this confusion appears to be the product of a deeper misunderstanding of the difference between stocks and flows and relationships between flows in economics. Those who conclude that this framework is really just an accounting structure are incorrect. Equally, those who conclude that the accounting relationships that are part of the sectoral balances framework are matters of interpretation are also incorrect. It should be clear that the sectoral balances framework combines accounting structures, which are derived from the national accounts framework used by statisticians to measure economic activity, and theoretical propositions, which seek to explain relationships between variables within the accounting structures. In other words, we need to understand both the accounting aspects that are true by definition as well as the underlying theoretical structures which drive the balances.…

Must-read relative to understanding MMT.

Bill Mitchell – billy blog
Flow-of-funds and sectoral balances
Bill Mitchell | Professor in Ecoof the Centre of Full Employment and Equnomics and Director ity (CofFEE), at University of Newcastle, NSW, Australia

Invisible hand-waving — Is economics a science?

Laktos v. Popper in philosophy of science applied to econ. First on a series on economics as science by two grad students at Oxford.

I think that rather than seeing science as just the sum of all scientific statements, we should view science as a web of research programmes, such as quantum mechanics or evolutionary biology. This is the view put forward by Imre Lakatos. Research programmes are essentially comprised of three parts: certain fundamental views and baseline assumptions which are unfalsifiable (the ‘hard core’), some rough rules of how and how not to go about solving scientific problems (‘positive and negative heuristics’), and a set of hypotheses which scientists use to generate and test predictions (the ‘protective belt’). For example, in the Newtonian research agenda, the ‘hard core’ consists of unfalsifiable baseline theoretical assumptions such as ‘every action has an equal and opposite reaction’. There is a ‘positive heuristic’ towards mechanistic, materialistic explanations of natural phenomena (hence Newton’s problems explaining gravitational forces). The ‘protective belt’ would include hypotheses like the predicted motion of the planets.

For Lakatos, the hallmark of a ‘progressive’ scientific research programme is not whether it is falsifiable. In fact, the ‘hard core’ is unfalsifiable, and much of the protective belt is often falsified as scientists puzzle over how to accommodate various pieces of evidence. Rather, the hallmark of a progressive scientific research programme is whether or not the theory makes successful novel predictions. In other words, is the theory is able to successfully predict new phenomena which it was not originally built to explain?….

Invisible hand-waving
Is economics a science?

ht Mark Thoma at Economist's View

Also

Argues for Friedman's instrumentalism.

I think this grossly overlooks the motivation for pursuing knowledge in the first place. This putting forward the most reasonable explanation of change that answers the questions, how and why. In philosophy, this is based on self-evident first principles, which are called assumptions in economics. In science, the answers are tentative on testing through hypothesis testing. Friedman's instrumentalism is not science but philosophy.

In philosophy, the why question is answered by reasons, e.g., in accordance with the principle of sufficient reason. In science, on the other hand, the answer is expected to be in terms of causes, that is, answering the questions both why and how.

Correlation does not prove causation, and satisfactory explanation is about causation unless one accepts Hume's metaphysical assertion that causation is only constant correlation since human knowledge does not extend to causal mechanism. That is a philosophical position that is dogmatically asserted rather than a scientific one, and it is a contested one in theory of knowledge.

Causation is one of the deep issue of human intellectual history, debated for millennia. It is still unresolved. Friedman's instrumentalism is hand-waving. If this is the best that economists can do, they should just hang it up.

Assumptions, Milton Friedman and wise trees

Daniel Kahneman on correlation, causation and mean regression

It took Francis Galton several years to figure out that correlation and regression are not two concepts – they are different perspectives on the same concept: whenever the correlation between two scores is imperfect, there will be regression to the mean …

Causal explanations will be evoked when regression is detected, but they will be wrong because the truth is that regression to the mean has an explanation but does not have a cause.

Lars P. Syll
Regression to the mean – when causes trump statistics
quoting Daniel Kahneman | Professor Emeritus of Psychology and Public Affairs at Princeton University's Woodrow Wilson School and Nobel Laureate in Economics (2002)

Steve Roth — Note to Reinhart/Rogoff (et. al): The Cause Usually Precedes the Effect

More on the math.

By this standard of propter hoc analysis, R&R’s paper shows less analytical rigor than many posts by amateur internet econocranks. (Oui, comme moi.) This is a paper by top Harvard economists, and they didn’t use the most elementary analytical techniques used by real growth econometricians, and even by rank amateurs who are doing their first tentative stabs at understanding the data out there.

Asymptosis
Note to Reinhart/Rogoff (et. al): The Cause Usually Precedes the Effect — Or: Thinking About Periods and Lags
Steve Roth

Lars P. Syll — Probabilistic reasoning and its limits (wonkish)

Keynes.

Lars P. Syll's Blog
Probabilistic reasoning and its limits (wonkish)
Lars P. Syll | Professor of Social Studies and Associate Professor Economic History, Malmo University