Causal Inference with Machine Learning - EXPLAINED!

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Very instructive and well-made video !
I have 1 question: where can I found your video on this calibration thing ? Very curious about that !

I also have 1 slight remark: 12:08 into the video there is a mistake in the formula for ITE. In both terms of the ITE formula you use W=1, but this trivially has to be W=1 (treated) and W=0 (not treated) respectively I think. Do you agree ?

It is just a minor remark, the rest is outstanding 👍

scitechtalktv

Here is a video on how causal inference can go a long way with machine learning. It's a fun video from the foundation of the concept with some important math. Hope i lay this out right and it's easy to understand. Any thoughts? Let me know in the comments or discord (link in description). Cheers!

CodeEmporium

The two-model approach assumes your models have no error, which is optimistic at best.
Subtracting the two values from the treated model and the control model completely disregards that these point estimates are not accurate (unless you have perfect and therefore overfitting models, which is bad anyway).
The two-model approach should only be used as an example to show why modeling and measuring Uplift is not trivial and therefore requires specific tools like Uplift modeling techniques

maraffio

Cool topic! One confusion I had on the class transformation approach:
If W=0 and Y=0 how can we be sure this is a "persuadable" and not a "lost cause"? If I understand correctly, both groups can take on these values. Similar question when W=1 and Y=1, can we be sure this is a "persuadable" and not a "sure thing"?

ritvikmath

great video! Thanks!
FYI: at 12:20 ITE formula is wrong. Both terms are same (prob of customer purchased given emails) but latter should be prob of customer purchased NOT given emails

gutihernandez

Intuitively, since z can only have value 1 or 0, the treatment effect on the persuadable group p(z=1) wrt other groups p(z=0) is p-(1-p) = 2p-1. Great work, thanks!

jingwangphysics

great explanation! Pls continue making videos on these topics “causal inference” as there are very few informational videos.

soumen_das

Hi, I am in drug discovery and the things you talk about are directly translatable to my work (potential customer vs. patient who will respond best to my drug). Thanks so much!!

jeffreagan

Keep up your great work! You are such a good teacher (+ entertainer sometimes ;)).

TheMrKingplays

You said ITE is in the range [0, 1]. Can it not be negative for the "sleeping dogs" category you defined?

srijanmishra

good course, but I don't get how the data collected for Zi, because Zi = 1 when the sample (in treatment group and convert) OR the sample (in the control group and not convert). Persuadable should be the 'AND' between the above relationship, is it?

kesun

Looking forward to it. Causal Inference is one of the coolest ML ideas I've been able to ise

ChocolateMilkCultLeader

Nice explanation. Have you create any videos about dynamic causal inference with Machine learning

kasunthalgaskotuwa

Awesome!! I have a question. What if I don't have Randomized Data? How can I then estimate ICT and do Uplift Modelling?

arresteddevelopment

Whats is definition of
1. Model 1
2. Model 2
3. Z
?

muchidariyanto

please, can you make a video to explain the dual aspect collaborative attention ?

hassenhadj

Great video...keep it up...all the best

dubeya

Loved it!! Thank you for the derivation; simple when you explain it. I would've just accepted it as something handed down from the gods otherwise

wonjun

12:08 shouldn't it be:

P(Zi = 1 | Xi) = P(Yi = 1 | Xi, Wi = 1) + P(Yi = 0 | Xi, Wi = 0)

Instead of Wi = 1 and Wi = 0 being on the left, they should be on the right, or what am I missing?

dmtree__

Very good video. I have a question, what if the probability of treatment is not 0.5 but some other value, such as 1/4? Then you cannot fully merge the probability person Xi got the email at 13:52. In this scenario, the ITE = -1 + 4/3P(Zi=1|Xi) + 8/3P(Yi=1, wi=1|Xi). What should we do with the unmerged 8/3 P(Yi=1, wi=1|Xi)?

fangzheng

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