How to take out transaction of each block?

Excellent presentation.. Thank you!

If you could travel to the future from when you posted this video, you would come to a conclusion that in 2024 this is still extremely relevant. Great presentation!

i would like to make the option at 16:30 - pre-intervention, intervention and post-intervention, not only the classic pre and post period

He is amazing presenter! Many thanks

❤

Thank you for the presentation. Must be noted that the validity of this approach is entirely dependent on the ability to accurately extrapolate the control scenario based on observational data. In a nutshell, it relies on our ability to 'randomize' the treatment and control groups via adequate control variables. If, on the other hand, we miss a key variable in our extrapolation model, the estimated causal effect of the variable in question will be biased. This causal estimation is nothing but a way to approximate a randomized experiment scenario via a model which attempts to control for all relevant outcome drivers.

Such a great explaination! Thank you

Kay has very concisely explained what a super power a Marketing Analyst has to their exposure. He is a really good presenter.

Thanks, Kay Brodersen Rajavel KS, Bengaluru.

what if we only got post period, is it feasible to do it?

Great presentation 👏

Great video

окей, если несколько тритментов, почему мы не можем посчитать один, а потом его вычесть из временного ряда во втором тритменте, чтобы оставить влияние только одного изменения?

I was reading the reaseach paper but got to know about your video and my week work covered by your 30:38 mins. Amazing presentation!!!!

FANTASTIC Presentation!

What if we dont have a high correlated time series to train the model ¿

Cut off at the end =(

Can independent variables in the above example be considered to be instrumental variables?

This is so amazing

Wow. I have finally found the answers to what has been bugging my head for a long time. Thanks a lot!

Wow! Really cool library, amazing presentation. Can't believe I didn't know this was a thing.

Great presentation, it is not only a paper issue but an actual one. It really works.

I keep being amazed!

Fantastic talk! Thanks for sharing

Amazing presentation, I am currently working with this tool and you made things so much clearer, congratulations

Fantastic talk. Thanks for sharing

Really different approach! I really feel good when I love your presentation

very illustrative. thx

Hi Great job!!! Could you please tell me your email address or email me on naeemurself@gmail.com? I am also working on feature selection and I am want to know some about some problems personally. Thanks in advance.

This made my career

Best explanation on prescriptive I found so far. Thank you.

great talk and great questions from Q&A session. want to know more about how to choose the predictor time series.

A very clear presentation about the CAUSAL IMPACT tool! Thanks!

Great talk!

His way of hmm hmm..., speaking is just hmm hmm hmm frustating. I had to use x1.5 speed.

any way to get the data set of the link? it seems missing

excellent explanation!

Excellent Presentation on Causal Analysis

This man is a hack fraud.

One of the best presentations. Thanks a ton for explaining this so beautifully!

thanks

Dude this was incredible. Went through the whole work flow so well. Thank you.

I can't read that s-expression guys. Please help. I'm desperately trying to follow.

You, sir, are an outstanding presenter. This was perfect. Thank you for developing and releasing the package and thank you for providing this excellent presentation.

Is there a guide to design a model architecture: number of layers, nodes... ?

One of the best talk I've ever seen this is how you should explain things.

Acertar 14 de 16 - además de que está al alcance de cualquiera apostando a favor del Bayern, del Barsa o del Madrid en sus propias ligas... - no tiene aplicación práctica ninguna. Buscar el cien por cien, los 16 de 16, imagino que será entretenido, pero es que no es posible porque el fútbol es un juego de personas. Las personas, por otra parte, están necesitadas de seguridades. Juegos no han faltado nunca. S2.

This is very good example of how pergel works . Liked the way the pregel is explained.

You are the best 👍🏼