Showing posts with label correlation. Show all posts
Showing posts with label correlation. Show all posts

Thursday, August 17, 2017

Noah Smith — "Theory vs. Data" in statistics too


Important.

I think Noah has this right. Fit the tool to the job, rather than the job to the tool.

Aristotle defined speculative knowledge in terms of causal explanation. This definition stuck although Aristotle's analysis of causality did not.
In the Posterior Analytics, Aristotle places the following crucial condition on proper knowledge: we think we have knowledge of a thing only when we have grasped its cause (APost. 71 b 9–11. Cf. APost. 94 a 20). That proper knowledge is knowledge of the cause is repeated in the Physics: we think we do not have knowledge of a thing until we have grasped its why, that is to say, its cause (Phys. 194 b 17–20). Since Aristotle obviously conceives of a causal investigation as the search for an answer to the question “why?”, and a why-question is a request for an explanation, it can be useful to think of a cause as a certain type of explanation. (My hesitation is ultimately due to the fact that not all why-questions are requests for an explanation that identifies a cause, let alone a cause in the particular sense envisioned by Aristotle.) — Stanford Encyclopedia of Philosophy
There is a distinction between reasons and causes. Some types of explanation seek only reasons, while other seek causes. Causation subsequently came to be viewed in terms of articulating mechanisms or lines of transmission (models) that are substantiated in evidence.

Explanation by reasons is different since the strict criterion of articulating mechanisms or lines of transmission that can be checked against evidence is not required.

Explanation by reasons rather than strictly by establishing causation is based on the principle of sufficient reason, which is usually credited to Spinoza and Leibnitz.

In philosophical logic, two negative criteria are foundational. Valid reasoning is vitiated by 1) arguing in a circle and 2) infinite regress.

Without recourse to checking against evidence there is no stopping point in assigning causes other than stipulation, e.g. of a first cause.

However, there may be a reason for a stopping point that doesn't involve causality based on evidence from observation or only stipulation, for example, principles that are "self-evident" based on intuition such as Aristotle's conception of intellectual intuition, or Kant's synthetic a priori propositions as articulated in the Critique of Pure Reason

On the other hand, Hume argued that causality is merely over-interpretation of constant correlation, there being no knowledge of the world other than that based on sense data. There is no observable causal link.

Cutting to the chase, scientific explanation based on causality is grounded in models that articulate causal mechanisms or lines of transmission that show how things change invariantly, which is the basis for deterministic functions. Where this is not possible, then there are two other avenues. The first is explanation by giving reasons, which is the domain of speculative philosophy. The second is employing statistics to explore patters of correlation. The question then is to what degree causal models can be gained from statistical methods, or whether it is possible at all. 

This is the issue that Noah Smith's post is getting at.

Noahpinion
"Theory vs. Data" in statistics too
Noah Smith | Bloomberg View columnist

Tuesday, April 15, 2014

So-called US "national debt" (= savings accounts at the Fed) Has No Historical Correlation With Inflation.

Image hat tip to Auburn Parks at SeekingAlpha.

US National Savings Accounts co-plotted with CPI.



Can't repeat this viewing often enough. We have at least 10 million citizens to reach, just to start making a dent. 

Can someone please show this to Simpson & Bowles, and to Maya MacGuineas, at the "Committee for a Ridiculous Federal Budget"?

The only question remaining is who is sitting on the bulk of those currency assets? The distributed consumers/investors/users previously known as the US MiddleClass ...... or,  ..... a collection of oligarchs?


Sunday, June 30, 2013

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)

Wednesday, September 19, 2012

Repeated Loudly and Often Enough, a Self Deception Becomes Accepted Truth?

commentary by Roger Erickson

According to some commodity investment advisors, this is ...

How QE3 – Like QE1 and QE2 – Will Trigger Inflation.

Seems like no 2 people use the same, semantic definition for "inflation," but that word triggers the same fear in most, regardless of which definition is used. Let's call it inflated convergence.

I would bet $50 that there's a correlation between financial trades and the frequency of media mention of anything sounding remotely like "inflation."

Some psychology dept somewhere has likely done a test run with some local media outlets - sprinkling a variety of "be-, ce-, de-, fe-, ge- ... on to ze-flations" into ongoing business section articles. Maybe even "zen-flayshun," for those meditating on how to boycott self flagellation!

Wednesday, July 4, 2012

Lars P. Syll — Keynes’s critique of econometrics (wonky)

Unfortunately, economists often hold the view that Keynes’s criticisms of econometrics is the conclusions of a sadly misinformed and misguided intellectual who disliked and did not understand much of it. This is really a gross misapprehension. To be careful and cautious is not the same as to dislike. And as any perusal of the mathematical-statistical and philosophical works of people like for example Nancy Cartwright, Chris Chatfield, Hugo Keuzenkamp or Arios Spanos would show, the same critique is more or less put forward by respected authorities.
I would argue, against “common knowledge”, that Keynes did not misunderstand the crucial issues at stake in the development of econometrics. Quite the contrary. He knew them all too well – and was not satisfied with the validity and philosophical underpinning of the assumptions made for applying its methods.
Read it at Lars P. Syll' Blog
Keynes’s critique of econometrics
by Lars P. Syll | Professor, Malmo University