In other posts, I’ve explained what causation is and how to do causal inference using quasi-experimental designs (DID, ITS, RDD). Almost for all research methods, they have to meet two preconditions in order to generate meaningful insights: 1. the treated group looks like the control group (similarity for comparability); 2. a sufficiently... Read more

Background In a series of posts (here, here, here, here and here), I’ve explained why and how we should run social experimentations. However, it’s not possible to do social experiments all the time, and researchers have to identify causal effects by other observational and quasi-experimental methods. [Related Article: Causal Inference: An Indispensable Set of Techniques... Read more

Editor’s Note: Want to learn more about key causal inference techniques, including those at the intersection of machine learning and causal inference? Attend ODSC West 2019 and join Vinod’s talk, “An Introduction to Causal Inference in Data Science.” Data scientists often get asked questions of the form “Does X Drive... Read more

Two of the main techniques used in order to try to discover causal relationships are Graphical Methods (such as Knowledge Graphs and Bayesian Belief Networks) and Explainable AI. These two methods form in fact the basis of the Association level in the Causality Hierarchy (Figure 1), enabling us to answer... Read more

Thanks to recent advancements in Artificial Intelligence (AI), we are now able to leverage Machine Learning and Deep Learning technologies in both academic and commercial applications. Although, relying just on correlations between the different features, can possibly lead to wrong conclusions since correlation does not necessarily imply causation. Two of... Read more

Editor’s note: Robert Ness is a speaker for ODSC East 2021. Check out his talk, “Causal Machine Learning Blitz,” there! Causal modeling and inference are perhaps at the core of the most interesting questions in data science. A common task for a data scientist at a FAANG is to query... Read more

Erich is a speaker for ODSC East 2020 this April 13-17! Be sure to check out his talk, “Methods for Using Observational Data to Answer Causal Questions,” at this upcoming event! Is drinking red wine associated with decreased risk of heart disease? Does drinking red wine prevent heart disease? Should... Read more

Data scientists have tried to differentiate causality from correlation. Last month alone, I’ve seen 20+ posts referencing the catchphrase “correlation is not causality.” What they actually want to say is correlation is not as good as causality. [Related Article: Discovering 135 Nights of Sleep with Data, Anomaly Detection, and Time... Read more

Editor’s note: Alessandro Romano is a speaker for ODSC West this October 30th to November 2nd. Be sure to check out his talk, “The Crucial Role of Digital Experimentation and A/B Testing in the AI Landscape,” there! Collecting a considerable amount of data has become a regular part of our... Read more

There’s a lot to consider when learning about data science, such as what topics are relevant, format, and so on. To help you build AI better, we’ve created a list of the top ten virtual talks from ODSC West this year so you can learn a variety of topics and... Read more