Underground Secrets of Causality, Inference & Attribution

Aditya Yadav
Nov 24, 2019 · 8 min read

What is Causality?

Causality is efficacy, by which one process or state, a cause, contributes to the production of another process or state, an effect, where the cause is partly responsible for the effect, and the effect is partly dependent on the cause.

The Axiom of Causality

The Axiom of Causality is the proposition that everything in the universe has a cause and is thus an effect of that cause. This means that if a given event occurs, then this is the result of a previous, related event. … The magnitude of an effect is proportional to the magnitude of its cause.

The Cliche — Correlation is NOT Causation!!!

In statistics, the phrase “correlation does not imply causation” refers to the inability to legitimately deduce a cause-and-effect relationship between two variables solely on the basis of an observed association or correlation between them. The complementary idea that “correlation implies causation” is an example of a questionable-cause logical fallacy, in which two events occurring together are taken to have established a cause-and-effect relationship. This fallacy is also known by the Latin phrase cum hoc ergo propter hoc (“with this, therefore because of this”). This differs from the fallacy known as post hoc ergo propter hoc (“after this, therefore because of this”), in which an event following another is seen as a necessary consequence of the former event. — Wikipedia

So what is Correlation? (That which doesn’t imply causation?)

In the broadest sense correlation is any statistical association between two random variables or bivariate data, though it commonly refers to the degree to which a pair of variables are linearly related.

Please note the words — a pair of variables which are ‘Linearly’ related. Which means if one variable increases or decreases the other one increases or decreases OR (inverse linear relation) decreases or increases. We will discuss this later.

What is the best way to understand Causality?

  • Correspondence
  • Time Order
  • Non-Spuriousness
  • Mechanism(s)
  • Context

Are these 5 criteria necessary and sufficient? Yes! They are.

Correspondence

Case 1: Does HRT lower CHD?

Lets revisit the HRT and CHD example again and try to understand Causality.

The first step — the correlation.

Yes! thats been amply established. Women taking HRT do have a lower than average CHD.

What are Causal Graphs?

In statistics, econometrics, epidemiology, genetics and related disciplines, causal graphs (also known as path diagrams, causal Bayesian networks or DAGs) are probabilistic graphical models used to encode assumptions about the data-generating process. They can also be viewed as a blueprint of the algorithm by which Nature assigns values to the variables in the domain of interest.

College Education & Salary
The Model (Mechanisms)

The Issue: Linear vs Non-Linear Causality

View but ignore this video, we will explain the real concept of non-linearity

The Issue: Directed Acyclic Graphs (DAG’s) vs Directed Cyclic Graphs (DCG’s)

In any real world system there can be circular relationships in the causal graphs. The causal graphs are “directed” (which is the entire essence of time-ordered cause and effect) but they can be cyclic. (The concept of bi-directional relationships is implicitly subsumed in this construct)

The Issue: Discrete Relationships

The other issue with Causality is when the Relationship between cause and effect is NOT continous but rather discrete (as in like discrete digital signals). Such Causality is also next to impossible to establish.

Case 2: Computer Program

Let there be a computer program which shows fractals on the screen. And takes as input multiple parameters, which include the random number seed and some other size parameters.

Case 3: Neural Networks

Deep Learning is the craze today. One of the issues with it is explainability, how can it be explained that which data or parameters led to which conclusion.

Conclusion

We explained to you the essence of Causality, Attribution and Inference.

About us

This is our website http://automatski.com

Its42

The Meaning of Life, Universe & Everything

Aditya Yadav

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The Meaning of Life, Universe & Everything

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