# Launch Of Modeling Mindsets Book š

### How having more than one modeling mindset has helped me get unstuck on some modeling projects.

Todayās post is a bit shorter ā I injured my knee and I launched my new book, Modeling Mindsets - The Many Cultures Of Modeling Data, today.

So I write to you with both joy and pain.

Modeling Mindsets is my first book since I decided to become a full-time writer.

And it's the book I wish I had read a few years ago to save myself a lot of time.

## Getting Stuck In Modeling Projects

Earlier in my career, I sometimes got stuck in modeling projects. I donāt mean coding mistakes, even though a bug can cost you lots of time as well.

No, by āstuckā I mean that my approach to modeling didnāt even contain the right language to solve the project.

And the worst about it: At first I didn't even realize that it was because I applied the wrong modeling mindset. I studied statistics, which gave me an almost pure frequentist statistical modeling mindset. Back then, I had a narrow lens on modeling.

Here are two particular examples where I got stuck:

We studied the effect of a drug on a disease outcome. The drug didnāt seem to help, judged by the p-value of the coefficient. Maybe we used the wrong model and or the wrong distribution assumptions?

*As it turned out, the solution was using causal inference.*I tried my luck in my first Kaggle competition ā with my frequentist statistics mindset from university. So instead of random forests and cross-validation, I carefully crafted a generalized additive model using in-sample evaluation (I think R-squared), which turned out to produce bad predictions.

*The solution was to adopt a supervised learning approach.*

My first reaction was always to double down on my current mindset. You donāt want to know how many different GAMs I uploaded in my first Kaggle competition š¤£.

But as time went on, I realized that I had to approach the problem differently. If I had had at least an intuition about causal inference and supervised learning, I could have saved myself a lot of time back then.

# Understand Many Mindsets

My idea with the book Modeling Mindsets was to take a bird's eye view of approaches from Bayesian inference to unsupervised learning.

It can take years to develop a feel for a modeling mindset, as introductions are often loaded with math and theory.

Modeling Mindsets shines a light on core beliefs, strengths, and limitations of modeling approaches, all while being

short,

math-free,

intuition-first.

It's the book I wish I had read some years ago. And that, I guess, is always a good motivation to write a book.

**You can get the book directly from Leanpub or sign up here to get a 30% discount until Friday:**

If you think a colleague or a friend could also benefit from Modeling Mindsets, Iād appreciate it if you share the link!

I've purchased the book and I'm enjoying it so far. It's definitely going to be a reference to me as I progress through future modelling projects. You've introduced this concept of being a T-shaped modeller which I like, I'm going to adopt this approach in future. I'm reading the causal inference chapter and find myself in a similar position that you were in your TNF-alpha blockers project.

I wanted to ask if you have any good books/ materials on how one can get started with applying causal inference? This is a mindset that is going to be crucially important in the work I will be doing so I want to make sure I start off in the best possible way. I'm a statistician by training so I've also constantly been exposed to the "correlation isn't causation" mantra. Do you have any primers or materials that you suggest are good in order to learn this mindset and begin to apply these methods in projects?

Will the print or ebook version be black and white?