New exciting ideas that marry causality with generative modeling, conformal prediction and topology. — NeurIPS is considered one of the most important and prestigious conferences on artificial intelligence and machine learning globally due to its rigorous paper review process and high quality of accepted research. With its multidisciplinary focus, the conference covers an extensive range of topics related to developing intelligent systems and machine…

Applying synthetic control with a Bayesian twist to quantify the impact of a tweet (using CausalPy) — October 2022 brought a lot of novelty to Twitter’s Headquarters in San Francisco (and a sink). Elon Musk, the CEO of Tesla and SpaceX became the new owner and the CEO of the company on October 27. Some audiences welcomed the change warmly while others remained skeptical. A day later…

…and unlock the potential of the best Causal Discovery package in Python! — Introduction The recent surge in interest in causality-related topics in Python has led to a wealth of resources making a decision what to focus on challenging. For instance, many resources on the internet describe a popular NOTEARS algorithm (Zheng et al., 2018) as the “state-of-the-art structure learning method”, yet NOTEARS has…

…and you can get 3 of them completely for free if you want! 🤗 — Introduction Recent years brought a sharp increase in interest in causal methods in the research community and in the industry. One of the challenges that people entering the field face is a lack of standardized resources and terminology. Causality research has been scattered and divided into sub-fields for decades. One of…

Learn 3 techniques for causal effect identification and implement them in Python without losing months, weeks or days for research — If you’re reading this you’ve probably been in data science for some 2–5 years now. You have most likely heard about causality before, maybe even read a book or two on the topic, yet if you don’t feel confident or you’re missing some clarity on how to grab these concepts…

A Polish translation of an article, that we wrote with Mike Erlihson, PhD as a part of #DeepNightLearners series. — Scroll down for English and Hebrew versions. Other language versions: - English (original) - Hebrew Współautorem tego artykułu jest Mike Erlihson. W tłumaczeniu pomógł Mateusz Modrzejewski ❤️ W zasadzie dowolne dane mogę być reprezentowane jako wektor w ciągłej przestrzeni reprezentacji. Często jednak wektory dla różnych punktów danych koncentrują się w…

…and you can get them for free if you want! ❤️ — Introduction 2022 was a truly amazing year for the machine learning community worldwide! Many long-awaited titles have been released, including new editions of all-time classics. In this post I want to share with you three 2022 titles that I believe are especially worth reading (not only) this year. At the end…

Part 4: Going fully probabilistic¹ — This series is a brief introduction to modeling uncertainty using TensorFlow Probability library. I wrote it as a supplementary material to my PyData Global 2021 talk on uncertainty estimation in neural networks. Articles in the series: Part 1: An Introduction Part 2: Aleatoric uncertainty Part 3: Epistemic uncertainty Part 4…

Part 3: Epistemic uncertainty — This series is a brief introduction to modeling uncertainty using TensorFlow Probability library. I wrote it as a supplementary material to my PyData Global 2021 talk on uncertainty estimation in neural networks. Articles in the series: Part 1: An Introduction Part 2: Aleatoric uncertainty Part 3: Epistemic uncertainty Part 4…