this post was submitted on 29 Oct 2023
1 points (100.0% liked)
Machine Learning
1 readers
1 users here now
Community Rules:
- Be nice. No offensive behavior, insults or attacks: we encourage a diverse community in which members feel safe and have a voice.
- Make your post clear and comprehensive: posts that lack insight or effort will be removed. (ex: questions which are easily googled)
- Beginner or career related questions go elsewhere. This community is focused in discussion of research and new projects that advance the state-of-the-art.
- Limit self-promotion. Comments and posts should be first and foremost about topics of interest to ML observers and practitioners. Limited self-promotion is tolerated, but the sub is not here as merely a source for free advertisement. Such posts will be removed at the discretion of the mods.
founded 11 months ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
I have been wondering about this too. Never saw a single situation where causal modeling was practically applied.
I am thinking whether potentially there is a fundamental flaw here.
If you don't know what causes what but just observe the correlation then most likely you will never find out what is the underlying cause because the entire causation is so complicated or hard to find or lacking further data that it's just not feasible to figure it out.
If you know what causes what you can build your model accordingly. But in such a situation, the causality is usually not "perfect". For example: If A causes B every single time when B occurs, then B does not truly give you any further information at all, because apparently all information must be contained in A. If, however, B does occur only with a certain probability when A occurs then you again are back to not knowing exactly how causality works, and there are unkonwn factors you cannot account for.
Don't know, just some thoughts.
My backgrpund is in ux design and strategic planning and I've recently started learning about Causal ML (as part of learning ML).
I found that you already need to bring your assumption/reasoning of causation to the model as a flow chart, it's usually more than A leads to B. Causal ML then uses your data to predict the accuracy of your assumption.
Here's an obvious example:
If I have an A/B test of an ecommerce checkout, one blue and one red button. and in an a/b testing, red performs better. then a prediction model would learn that red performs better than blue.
In Causal ML, i would bring all factors in: background color, position, button color font, user & purchase information.
I can then create a first causal discovery model to come up with a network graph of the relation and then use causal ML to calculate the probability of effects.
Turns out, the color mages a difference for older shoppers because blue has lower contrast than red. so I could also choose another color with similar contrast for the same perfomance effect.
But I agree with you, a lot of that the thought work needs to be done before.