[DALL·E](https://labs.openai.com/)'s _steampunk painting of a data scientist reading a book about causal machine learning_.

Causal Machine Learning is off to a good start, despite some issues

Reviewing the first three chapters of the book Causal Machine Learning by Robert Osazuwa Ness.

September 12, 2022 · Yanir Seroussi

The mission matters: Moving to climate tech as a data scientist

Discussing my recent career move into climate tech as a way of doing more to help mitigate dangerous climate change.

June 6, 2022 · Yanir Seroussi

Building useful machine learning tools keeps getting easier: A fish ID case study

Lessons learned building a fish ID web app with fast.ai and Streamlit, in an attempt to reduce my fear of missing out on the latest deep learning developments.

March 20, 2022 · Yanir Seroussi

Analysis strategies in online A/B experiments: Intention-to-treat, per-protocol, and other lessons from clinical trials

Epidemiologists analyse clinical trials to estimate the intention-to-treat and per-protocol effects. This post applies their strategies to online experiments.

January 14, 2022 · Yanir Seroussi
If you don't think about your modelling context, you're gonna have a bad time.

Use your human brain to avoid artificial intelligence disasters

Overview of a talk I gave at a deep learning course, focusing on AI ethics as the need for humans to think on the context and consequences of applying AI.

November 22, 2021 · Yanir Seroussi

Many is not enough: Counting simulations to bootstrap the right way

Previously, I encouraged readers to test different approaches to bootstrapped confidence interval (CI) estimation. Such testing can done by relying on the definition of CIs: Given an infinite number of independent samples from the same population, we expect a ci_level CI to contain the population parameter in exactly ci_level percent of the samples. Therefore, we run “many” simulations (num_simulations), where each simulation generates a random sample from the same population and runs the CI algorithm on the sample....

August 24, 2020 · Yanir Seroussi

Software commodities are eating interesting data science work

The passage of time makes wizards of us all. Today, any dullard can make bells ring across the ocean by tapping out phone numbers, cause inanimate toys to march by barking an order, or activate remote devices by touching a wireless screen. Thomas Edison couldn’t have managed any of this at his peak—and shortly before his time, such powers would have been considered the unique realm of God. Rob Reid After On Being a data scientist can sometimes feel like a race against software innovations....

January 11, 2020 · Yanir Seroussi

A day in the life of a remote data scientist

Earlier this year, I gave a talk titled A Day in the Life of a Remote Data Scientist at the Data Science Sydney meetup. The talk covered similar ground to a post I published on remote data science work, with additional details on my daily schedule and projects, some gifs and Sydney jokes, heckling by the audience, and a Q&A session. I managed to watch it a few months ago without cringing too much, so it’s about time to post it here....

December 11, 2019 · Yanir Seroussi

Bootstrapping the right way?

Bootstrapping the right way is a talk I gave earlier this year at the YOW! Data conference in Sydney. You can now watch the video of the talk and have a look through the slides. The content of the talk is similar to a post I published on bootstrapping pitfalls, with some additional simulations. The main takeaways shared in the talk are: Don’t compare single-sample confidence intervals by eye Use enough resamples (15K?...

October 6, 2019 · Yanir Seroussi

Hackers beware: Bootstrap sampling may be harmful

Bootstrap sampling techniques are very appealing, as they don’t require knowing much about statistics and opaque formulas. Instead, all one needs to do is resample the given data many times, and calculate the desired statistics. Therefore, bootstrapping has been promoted as an easy way of modelling uncertainty to hackers who don’t have much statistical knowledge. For example, the main thesis of the excellent Statistics for Hackers talk by Jake VanderPlas is: “If you can write a for-loop, you can do statistics”....

January 7, 2019 · Yanir Seroussi