The Book of Why by Judea Pearl

"Correlation is not causation." This mantra, chanted by scientists for more than a century, has led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, instigated by Judea Pearl and his colleagues, has cut through a century of confusion and established causality--the study of cause and effect--on a firm scientific basis. His work explains how we can know easy things, like whether it was rain or a sprinkler that made a sidewalk wet; and how to answer hard questions, like whether a drug cured an illness. Pearl's work enables us to know not just whether one thing causes another: it lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and key to artificial intelligence. Anyone who wants to understand either needs The Book of Why.

That's only one area of nonparametric. Nonparametric models are important for small data too where you don't or can't assume a distribution.

But I do agree with your sentiment that having a CI for your prediction is important.

To be fair to ML, there are area where they are very good at and that's data with low noises (images, NLP, etc..). I believe Frank Harrell's book Regression Modeling Strategies talk about this and his view on ML.

I also believe ASA have talk about how to augment ML with statistic for their goal for 2020.

If I can find ASA's post I'll update this post.

I don't know if this fall under nonparametric models but bootstrap get the distribution from data and not some assume distribution. It's certainly nonparametric technique at least. There are situation where bootstrap would be better than it's parametric counterpart.

That's not how causality works.

• if you were in control of data collection, you need to separately collect data for a control group and treatment group. The application of an intervention to the treatment group makes causal inferences basically trivial.
• if this is observational data that you are trying to do causal inference on post hoc, it's not impossible but way more complicated. Often involves approximating a "conditional average treatment effect" (CATE) by separately modelling individuals "matched" into a simulated control group vs a simulated treatment to estimate "counterfactual" phenomena.

Causal modelling is a whole rabbit hole. Check out this book for an intro: Judea Pearl - The Book of Why

• Naked economics: A intro level textbook but in a book so no math problems.
• The model thinker: About modeling systems.
• The selfish gene: evolutionary game theory is very similar to normal game theory

Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics) is a good text (and in my opinion the best out there) on time series forecasting.

It goes over both the theory and practice adequately. It also has R code which you can use, which is always a plus.

Yes. This book: Applied Predictive Modeling

Model Thinker does a good job of relating mathematical models to real life scenarios. The later chapters may help you fall asleep quicker https://www.amazon.com/Model-Thinker-What-Need-Know/dp/0465094627? Scorecasting is a light read too https://www.amazon.com/Scorecasting-Hidden-Influences-Behind-Sports/dp/0307591808?

The Book of Why
https://www.amazon.com/Book-Why-Science-Cause-Effect/dp/046509760X

Statistical Rethinking
https://www.amazon.com/Statistical-Rethinking-Bayesian-Examples-Chapman/dp/1482253445

That's the doctor who got arrested in London last year in the middle of his anti-lockdown speech. They took the book he was waving around, which is why we should read it.

https://www.amazon.com/gp/product/1645020576/

It's not a matter of computing power necessarily. We miss a piece of the theoretical puzzle here.

Check out this book if you're interested in where the biggest innovations still have to be made in computer science:
https://www.amazon.com/Book-Why-Science-Cause-Effect/dp/046509760X

https://www.amazon.com/Time-Analysis-Its-Applications-Statistics/dp/144197864X

There are some things I don't like about this book (e.g. a lack of exercise solutions available on the internet for the latest edition), but what's important about it relative to the others suggested here is that it covers state space models. State space models subsume basically every other common time series model as a special case. It's fine and proper to learn about the billion flavors of ARIMA model, but that should ultimately culminate in their abstraction: state space models.

A couple of initial questions to better inform recommendations:

1. Have you ever taken a probability/statistics course, or are you planning to entirely self-study?
2. Which programming languages do you know and use at a proficient level? I ask because this will be crucial for finding a biostats/stats job. Ideally, you should aim to gain some experience with R, Python, SQL, SAS etc. The choice of which programming language(s) you should focus on depends on the types of jobs you are targeting. SAS gets a lot of hate on this subreddit, but it is quite commonly the tool of choice for biostats. Stata is also used in some biostats teams too, albeit not nearly as common as SAS and R. You should start searching for biostats/stats job to gain a better sense of the common requirements, as they typically list the desired programming applications.

Yes, I think Casella/Berger would be a good start. However, this just scratches the surface... Again, depending on the specific jobs you are targeting, that would help inform which books to read. For biostats, I would recommend Regression Modeling Strategies by Frank Harrell. In general, it would be helpful to also read through an applied regression textbook, such as Kutner et al. Honestly, there are so many books which could be recommended. Once you provide some additional details (more specifics on your background in stats and target jobs), I am happy to follow-up with more recommendations.

Here’s another interview with Dr. Bhakdi that’s worth watching. This man is a world renowned microbiologist and virologist. His warning to the oral I chilling. I would so recommend his book co written with his wife, Corona: False Alarm, Facts and Figures.

https://youtu.be/0IBv3A8-ShI

https://www.amazon.com/Corona-False-Alarm-International-Bestseller-ebook/dp/B08JCDV25M

Also other books if you want to support fellow sceptics.

Alex Berenson https://www.amazon.co.uk/Books-Alex-Berenson/s?rh=n%3A266239%2Cp_27%3AAlex+Berenson

Sucharit Bhakdi https://www.amazon.co.uk/Corona-False-Alarm-Facts-Figures/dp/1645020576

It's great to see this. Deep learning models currently rule the world, but they are based entirely upon statistical correlation, and don't have representations of objects or classes and the causal relationships between them. In the meantime, the science of causal reasoning has been making serious progress in the past decade or two. They will need to be combined in order to create grounded models that can generalize.

For those of you who have not been following causal reasoning, I'd recommend the following books:

• J. Peters, D. Janzing, and B. Schölkopf. Elements of Causal Inference - Foundations and Learning Algorithms. MIT Press,Cambridge, MA, USA, 2017
• Pearl, J. Causality: Models, Reasoning, and Inference, 2nd. Cambridge University Press, New York, NY/ 2013

For a more popular and historical account, you can read:

• Pearl, J. The Book of Why

Yes, Yes I do: please read this book https://www.amazon.ca/Corona-False-Alarm-International-Bestseller-ebook/dp/B08JCDV25M/ref=sr_1_3?dchild=1&keywords=coronavirus+facts&qid=1609536370&s=digital-text&sr=1-3

Or listen to the recent podcast

1582 - Alex Berenson - Joe Rogan

and please then tell me what you think.

I mean just a few days ago, the finance minister in Ontario quit his job because 'while posting on twitter to stay home' he was on the beach in dominican republic.

Here's a great, and cheap book to order:

Coronavirus False Alarm

You can Google Dr. Sucharit Bhakdi, and watch a few of the interviews he has given. They are fantastic.

>Just because someone has the title of professor, doesn't mean they are right all the time.

Well, duuuh.

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>Anyone who goes against this, needs to show evidence that this is not the case

He did.

And, as a layman, I find his arguments very compelling!

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>... and win a nobel prize along the way.

How naive of you to think the establishment always abides by the science rather than going for political and/or corporate agendas.

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>Are you one of those anti-vaxxer people who never prove their own contrary opinion, but are always expectant of others to prove THEM wrong?

I always thought of anti-vaxxers as kooks... until 2020.

With all this shit going on in the world, I started doing some serious research into the history of vaccines and was surprised to learn that anti-vaxxers to raise some valid points.

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>How about the fact that this guy is the only one who has this opinion

What makes you think he's the only one?

You seem to be confusing consensus through censorship of dissident opinions with actual scientific consensus.

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>This professor = Presumed MORON

Presuming things about a person you know literally nothing about betrays a very strong bias.

If I were you, I'd cancel my order and re-order it.

The book is available on Amazon again :
https://www.amazon.com/Corona-False-Alarm-International-Bestseller/dp/1645020576

> Please learn how vaccines and MRNA vaccines work with the human body, thanks. If you actually understand microbiology maybe you wont have to turn to Facebook and Twitter conspiracy.

The views I expressed on this particular matter are largely based on a book written by a microbiologist who used to be professor at the University of Mainz, and his wife, who herself is a biologist and former associate professor at the University of Kiel. You can find the book here on Amazon.

Why makes you think you know better than them?

A got some news for you, there was no first wave. Before you freak out, please read this and then you and I can have a conversation between TWO informed people.

I just started reading this book, The Book of Why by Judea Pearl.

> Pearl's work enables us to know not just whether one thing causes another: it lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and key to artificial intelligence. Anyone who wants to understand either needs The Book of Why.

I first heard about him from a Sam Harris interview. There are a few talks by Pearl on youtube also.

Ultimately the answer is "We don't know. Therefore God exists." LOL. Sorry. Bad joke.

There does seem to be a difference between the quantum level and our macro or "emergent" level where we live and think. At least that is what we experience.

> I meant a reason as in if it happened in any other way it potentially wouldn't be better than it is right now.

We seem to be constrained by the arrow of time if we are wanting to change the past. Could past events have been different than they were? I don't think so. I think they are what they are (they were what they were). We cannot change them from our position today and we could not have changed them from our position in the past.

Not finished with it yet, but so far Judea Pearl’s the Book of Why is really good too. His research and philosophy is extremely unique IMO bec ause he is a computer scientist by training educated in Machine and deep learning, but a lot of his work has focused on understanding causality. The book discusses why causality is so important and the need for us to solve that problem before we can get computers to pass the Turing Test. IMO extremely relevant to I/Os attempting to blend theory with AI.

https://www.amazon.com/Book-Why-Science-Cause-Effect/dp/046509760X

I really like the Gelman text as well but honestly, I have begun to prefer Regression Modeling Strategies from Harrell, Jr. This book focuses on the "Hmisc" package and the 2nd edition was released within the last couple of years.

In their book, Applied Predictive Modeling, Kuhn and Johnson write (page 95):

> When the outcome is a number, the most common method for characterizing > a model’s predictive capabilities is to use the root mean squared error > (RMSE). This metric is a function of the model residuals, which are the observed > values minus the model predictions. The mean squared error (MSE) is > calculated by squaring the residuals and summing them. The RMSE is then > calculated by taking the square root of the MSE so that it is in the same > units as the original data. The value is usually interpreted as either how far > (on average) the residuals are from zero or as the average distance between > the observed values and the model predictions.

As you mentioned, there are other possibilities. Every measure puts the emphasis differently.

I didn't know about MLR until this post. So without having spent any time with it whatsoever, I would only say that one of the nice things about the caret package is that you can also leverage Kuhn and Johnson's book, Applied Predictive Modeling, as well as YouTube videos of Max Kuhn discussing <code>caret</code>.

> technically

Inevitable, at first, unless you're either lucky or playing it very safe (boring).

> scientifically

I've seen it. It is brutal to see somebody work incredibly hard, for years, to collect meaningless data. Design your experiment, harass your supervisor to find the flaws, harass any other PI you have contact with, make contact with the smartest PI you can find and ask them to critique your design, schedule meetings with each and every post-doc in the department, ask your grandma if it makes sense, ask the janitor, give a proposal presentation to another department, scour the literature from 1950 until yesterday, read this cover to cover, put this on your desk and peer into the relevant chapters, take a week off and ride your bike 500 miles, and then, and only then, start getting your samples together.