[HN Gopher] ML Beyond Curve Fitting: An Intro to Causal Inferenc...
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ML Beyond Curve Fitting: An Intro to Causal Inference and Do-
Calculus (2018)
Author : lnyan
Score : 65 points
Date : 2021-06-21 09:02 UTC (1 days ago)
(HTM) web link (www.inference.vc)
(TXT) w3m dump (www.inference.vc)
| benibela wrote:
| Recently I finished a PhD about causal inference. How can that
| help my life or career?
| clircle wrote:
| What are the greatest successes of causal inference? What
| problems have been solved by the do-calculus or potential
| outcomes frameworks?
|
| I'm pretty sure the link between smoking and cancer was
| established before causal inference came about.
| fny wrote:
| Propensity score matching is probably the one area with the
| greatest utility. There's a lot of good literature on the
| subject and it's fairly popular in econometrics and health
| analytics.
| Fomite wrote:
| Keep in mind that the timeline from medical research being
| published to entering practice can sometimes be measured in
| decades, and causal inference is swimming against the RCT-heavy
| currents of medical research. Several journals I publish in,
| for example, expressly forbid causal language for non-RCTs
| (much to my consternation as a mathematical modeler).
|
| Many of the tools of causal inference were also relatively
| inaccessible until fairly recently.
|
| I'd argue that the potential outcomes frameworks are really
| useful from a philosophical standpoint in teaching students,
| and things like the target trial framework has been doing
| useful work in conceptualizing observational studies in the
| context of a hypothetical trial (and recognizing that trials
| are themselves essentially a special case of cohort studies).
|
| The major source of potential is the places where you can't
| ethically randomize interventions, yet still need to produce
| evidence.
| [deleted]
| clircle wrote:
| Thanks for this. I agree that potential outcomes is very
| useful, at least on a pedagogical level. I have been reading
| Imbens and Rubin's book and loving it.
| yenwel wrote:
| I think causal inference is succesfull where it is impossible
| do an intervention to force a randomized trail on your
| population. Classical statistics like in agriculture that you
| set up a design and field trail to find out the interactions
| and additive effects is sometimes not possible. Say you want to
| check the effect of some economic policy change or medical
| treatment that would be unethical to refuse to some part of the
| population.
| cracker_jacks wrote:
| Can you give an example of how you can get away without an
| intervention?
| pjmorris wrote:
| Have a look at the literature on how smoking was
| established as a cause for cancer. You can't ethically
| intervene to have non-smokers smoke long enough to develop
| lung cancer. A lot of money and intellectual effort was
| spent on correlation not equaling causation in this case.
|
| I'm no expert on the literature here, but Peter Norvig
| mentions the smoking-cancer example in his article on
| experiment design [0]. He gets to the same place the
| causality people do; observational studies.
|
| [0] https://norvig.com/experiment-design.html
| efm wrote:
| I recommend the book (free online):
| https://www.hsph.harvard.edu/miguel-hernan/causal-
| inference-... and the associated Coursera course. It's both
| simple and subtle to be able to get causality out of
| observational data.
| 0101010110 wrote:
| This is true only for a small subset of Causal DAGs even
| within this 'Causal Calculus'. It can't account for
| circular causality or discontinuous relationships. That's
| not to diminish your suggestion, only to contextualise
| it.
| benibela wrote:
| The are new theories for cycles:
| https://www.eur.nl/sites/corporate/files/2018-07/mooij-
| pup-2...
| dwohnitmok wrote:
| You can't really, at least not in the sense that I think
| most people think of it.
|
| You basically need to make some assumptions that are
| broadly equivalent to assuming you've already correctly
| guessed certain parts of the underlying causal structure.
| So in a certain sense you're kind of begging the question,
| in a way that you wouldn't need to do if you had the
| ability to do interventions/randomized trials.
|
| That being said causal inference techniques are still very
| valuable in making explicit exactly what assumptions you're
| making and how those affect your final conclusion and
| therefore how to minimize the impact of those assumptions.
| 0101010110 wrote:
| The rules also provide a framework within which you can
| rule out some causal relationships. So they at least go
| some way to confirming which hypotheses can't be correct
| given the data.
| labcomputer wrote:
| At a high level:
|
| The core idea behind a RCT is that the characteristics of a
| "unit" (a patient) can't affect which treatment is
| selected. On average, people who got treatment A are
| statistically the same as those who got treatment B. So you
| can assume any difference in outcome is a result of the
| treatment.
|
| One of the simpler ways to do causal inference is by
| pairwise matching:
|
| You try to identify what variables make patients different.
| Then find pairs of units which are "the same" but received
| different treatments. After the pairing process, your
| treatment and control groups should ("should" is doing some
| heavy lifting here) now be statistically "the same" _by
| construction_. Recall, that this is what we were going for
| in an RCT. If you did everything right, you can now apply
| all the normal statistical machinery that you would apply
| to an RCT.
|
| The challenge is:
|
| 1. Identifying all the variables that make units alike.
|
| 2. You tend to throw away a lot of data, which reduces your
| statistical power. Even when the treatment classes are
| balanced, a given unit in class A may not pair up well with
| any unit from class B.
|
| 3. (Related to 2) Finding globally-optimal pairs of closest
| matches can be hard.
|
| 4. (Also related to 2) You need _at least some_ people in
| each group. Sometimes the treatment and control are just so
| different that _nobody_ pairs up very well.
|
| In some sense, the pairing process is just a re-weighting
| of your data. People who are similar to someone in the
| other group have a large weight. People who are unlike the
| other group have a low weight.
|
| You can generalize that idea a bit and reinvent what's
| called Inverse Propensity Score Weighting. In this case,
| you try to model a unit's propensity to receive a
| treatment, and then use 1/propensity as that unit's weight.
|
| The intuition is: If the model says you were likely to
| receive treatment B (you have a low propensity for A) and
| you actually received treatment A, then you are likely to
| pair up with someone who actually received B. So we should
| up-weight you.
| clircle wrote:
| I'm aware that causal inference is a popular technique in
| econometrics, and other places where we cannot conduct
| experiments. What I'm not aware of, is if these techniques
| have produced highly useful and reliable inferences. (Putting
| on my counterfactual hat) Are there examples of observational
| studies that would have failed to change public policy
| without the techniques of causal inference?
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