
Actual Causality (2016) - Tomte
https://www.cs.cornell.edu/home/halpern/papers/causalitybook-ch1-3.html
======
xtacy
I think this is a very important area with applications to legal issues; in
the computer systems domain, a close application I can think of is
troubleshooting / root-cause analysis.

In legal situations and troubleshooting alike, one is often interested not in
the "general" causes of why things happen (e.g., an increase in load will
contribute an increase in latency), but causes that explain specific cases
(e.g., the increase in latency on this incident was because of an increase in
file system latency at the backend.)

These concepts are quite interesting; one linked article discusses many
variations and refinements in detail:
[https://plato.stanford.edu/entries/causation-
law/](https://plato.stanford.edu/entries/causation-law/). I found these useful
mental models to organize my thoughts when troubleshooting systems and doing
root-cause analysis:

\- But-for cause: We can say that A is the actual cause of B when the
following holds: If not for A, not B. To avoid pathologies like "If not for
_big bang_ , this B wouldn't have happened", we have ...

\- Proximal cause: In the causal chain of explanations: If not for A1, not A2;
if not for A2, not A3, ..., the proximal cause of A(k) is the closest, i.e.,
A(k-1).

\- Necessary element of a sufficient set (NESS criterion): Things get
interesting here if we need a conjunction of many events to happen
simultaneously for a desired effect. For simplicity, assume that events are
boolean, and causal relationships can be defined using boolean formulae. Here,
A is a cause of B in the NESS sense, when B = (A and C1) OR (C2 and C3) OR ...
A here is "necessary" in the sufficient set {A, C1} for B to happen.

~~~
whatshisface
Okay, so this is going to be a contraversial question but... How is any of
that not obvious to someone who is investigating the cause of something?

~~~
lmm
Most people have some vague sense that these aspects are important, and would
probably come up with a similar taxonomy if pressed, eventually. But getting
the details right and using consistent terminology matters; we don't usually
notice how woolly our thinking is until we make a real effort to be precise.
Think of something like the SemVer standard: "everyone knew" this was what
version numbers meant, but writing it out explicitly in a precise way was
actually really valuable.

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dwheeler
Thanks for the link, I've started reading it.

The complexity of defining and determining "actual causality" is a good
justification for mathematicians' avoiding the whole thing and using material
implication instead. As most of you already know, material implication A -> B
("A implies B") is a boolean operation, which is the same as ((not A) OR B),
see
[https://en.wikipedia.org/wiki/Material_implication_(rule_of_...](https://en.wikipedia.org/wiki/Material_implication_\(rule_of_inference\))
. Material implication avoids all this complexity.. but because it's simpler,
it _cannot_ by itself represent our notions of causality. Material implication
even has some "weird" surprises, e.g., "All Martians are green" and "All
Martians are not green" are both true when defined using material implication.
I created an "allsome" quantifier to deal with this problem:
[https://dwheeler.com/essays/allsome.html](https://dwheeler.com/essays/allsome.html)
. But it'd be nice to learn about other alternatives, so I started reading
this.

I've only started reading this book, but the approach does seem a little odd.
Its definition of causality embeds a model, which has embedded causality in
it. In section 2.1
[https://www.cs.cornell.edu/home/halpern/papers/causalitybook...](https://www.cs.cornell.edu/home/halpern/papers/causalitybook-
ch1-3.html#head2.2) it notes this issue: "It may seem somewhat circular to use
causal models, which clearly already encode causal relationships, to define
causality. There is some validity to this concern... Nevertheless, I would
claim that this definition is useful."

I don't have a better idea, and I can see the value in making the use of
models clear. But it does seem to weaken the idea of defining causality. Is
this considered reasonable? Is there a possibly better approach?

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dropalltables
Thanks for sharing. I'm a sucker for anything advancing causal reasoning
amongst technical folks :)

~~~
adaszko
Me too! Any other resources you can share?

~~~
mjirv
Anything by Judea Pearl, but especially the Book of Why[1], is good. He comes
at causality from a CS perspective, which I think would make sense for most
people on here.

Economists also have a big causality literature which might be less accessible
for HN folks but I think is still interesting and important. For a good intro
to all that, I suggest Scott Cunningham’s “Causal Inference: The Mixtape.”[2]

[1]
[https://www.indiebound.org/book/9780465097609](https://www.indiebound.org/book/9780465097609)

[2]
[https://scunning.com/causalinference_norap.pdf](https://scunning.com/causalinference_norap.pdf)

~~~
zwaps
Pearls model based causality is from a set up the same as structural
econometric models of the economists. Here, economists largely care about
identification and partial identification (check work by Manski), which is at
the same time related to causation but also a more practical matter of data
and model choice.

Very interesting stuff all this

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6gvONxR4sf7o
I'm familiar with the usual potential outcomes framework, and some of the high
level machinery of Pearl's version too, but this 'type causality' vs 'actual
causality' mentioned in the intro seems like a very unnecessary complication
that just confuses things.

They use "smoking causes lung cancer" as an example of type causality. A
counterpart in actual causality would be "Dave's smoking caused him to get
lung cancer." Then they go on to talk about how these are such different kinds
of causality.

They're no more different than "Dave has $35 in his pocket" and "the average
american has $21 in their wallet."

Seems totally unnecessary. Am I missing something?

~~~
CrazyStat
Perhaps Dave is suing his employer for compensation because he developed
cancer after working with material which was later found to be carcinogenic.
Dave's employer argues that since Dave was a smoker, his smoking caused his
lung cancer, and they are not liable. Dave admits thats smoking causes lung
cancer, but argues that it did not cause his lung cancer.

~~~
6gvONxR4sf7o
Right, and that fits in the usual potential outcomes framework without needing
to distinguish two types of causality.

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jpt4
Intermittently 404.

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juskrey
A book on casualty with no any single hint on mutual information.. hm

~~~
CrazyStat
Not surprising, since mutual information is a fundamentally non-causal
concept. This is easily seen by nothing that the mutual information of X and Y
is the same as the mutual information of Y and X, which means that mutual
information can't distinguish between "X causes Y" and "Y causes X".

~~~
skat20phys
There are causal accounts that use mutual information and its variants though.
A lot of them introduce notions of directed mutual information and other
similar concepts.

