Discussing the article: "Causal inference in time series classification problems"
Very good, sensible article.
Marketnig instead of marketing (the end of the penultimate paragraph of the introduction) sounds a bit er... intolerant).
Not bad.
In medicine randomised is that out of 1000 patients 60 are chosen at random, although what is available in the hospital are the candidates, blind that control test and placebo patients don't know which group they are in, neither do the treating staff. Well, and placebo.
There is no placebo.)))
And ATT there is no deciphering and translation, by meaning it is the average treatment of those treated.))))) It would be good ))))
There is no placebo.)))
And ATT there is no deciphering and translation, by meaning it is the average treatment of those treated.))))) That would be good ))))
ATT is the average difference in potential outcomes among the treated group only, yes. What percentage were cured and what percentage were not. Average treatment effect on treated stands for.
I realised, it's not in the text of the article, it's just an abbreviation without decoding).
I realised, it's not in the text of the article, it's just an abbreviation without deciphering).
Well it says at the top above the equation that it's for the treated. In general, the focus is shifted to the other side a little bit, so I did not describe it ) Specifically, how to adapt this science with strange medical definitions to BP analysis
It's hard to adapt. Rows - patients is hard. In parts only, but the difference of properties is big enough to make semantic transfers without explanations)))))
Besides, as I wrote before, that this is not an explicitly understood connection, but one found through experiments and not understood. I would add quasi causal inference for honesty.it's difficult to adapt. rows - patients are difficult. Only in parts, but the difference of properties is big enough to make semantic transfers without explanations)))))
Besides, as I wrote before, this is not an explicitly understood connection, but one found through experiments and not understood. I would add quasi causal inference for honesty.
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Check out the new article: Causal inference in time series classification problems.
In this article, we will look at the theory of causal inference using machine learning, as well as the custom approach implementation in Python. Causal inference and causal thinking have their roots in philosophy and psychology and play an important role in our understanding of reality.
Alison Gopnik is an American child psychologist who studies how infants develop models of the world. She also collaborates with computer scientists to help them understand how human infants construct common sense concepts about the external world. Children use associative learning even more than adults, but they are also insatiable experimenters. Have you ever seen a parent trying to convince their child to stop throwing toys around? Some parents tend to interpret this behavior as rude, destructive or aggressive, but kids often have other motives. They conduct systematic experiments that allow them to study the laws of physics and the rules of social interaction (Gopnik, 2009). Infants as young as 11 months prefer to experiment with objects that exhibit unpredictable properties rather than with objects that behave predictably (Stahl & Feigenson, 2015). This preference allows them to effectively build models of the world.
What we can learn from babies is that we are not limited to observing the world, as Hume assumed. We can also interact with it. In the context of causal inference, these interactions are called interventions. Interventions are at the heart of what many consider the Holy Grail of the scientific method: the randomized controlled trial (RCT).
But how can we distinguish an association from a real causal relationship? Let's try to figure it out.
Author: Maxim Dmitrievsky