Inferring the effect of an event using CausalImpact by Kay Brodersen 

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!

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

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

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.

FANTASTIC Presentation!

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

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

Excellent Presentation on Causal Analysis

I keep being amazed!

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!!!!

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

He is amazing presenter! Many thanks

Fantastic talk! Thanks for sharing

Such a great explaination! Thank you

Excellent presentation.. Thank you!

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

Fantastic talk. Thanks for sharing

Thanks! I like the summary() function!

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

excellent explanation!

Great talk!

This is so amazing

Thanks, Kay Brodersen Rajavel KS, Bengaluru.

Great video

This made my career

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

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.

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

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

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

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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?

How is CasualImpact different from a common marketing mix modelling project?