Causal

Evaluating ad targeting product using causal inference: propensity score matching!Photo by Tech Daily on UnsplashEver caught those pumped-up Nike Ads while tuning in to a podcast recapping last night’s epic NBA showdown? Or how about stumbling upon New Balance ads mid-sneaker review extravaganza on YouTube? That’s the magic of contextual targeting — the matchmaking maestro connecting content and ads based on the vibe of the moment! Say goodbye to ad awkwardness and hello to tailored ad experiences that’ll make you do a…

A step-by-step guide in understanding independence and how to apply it to validate directed acyclic graphs in causal validation using…Continue reading on Towards Data Science » A step-by-step guide in understanding independence and how to apply it to validate directed acyclic graphs in causal validation using…Continue reading on Towards Data Science » FOLLOW US ON GOOGLE NEWS Read original article here Denial of responsibility! Techno Blender is an automatic aggregator of the all world’s media.…

“The Book of Why” Chapters 3&4, a Read with Me seriesContinue reading on Towards Data Science » “The Book of Why” Chapters 3&4, a Read with Me seriesContinue reading on Towards Data Science » FOLLOW US ON GOOGLE NEWS Read original article here Denial of responsibility! Techno Blender is an automatic aggregator of the all world’s media. In each content, the hyperlink to the primary source is specified. All trademarks belong to their rightful owners, all materials to their…

Causal inference can help you become a business analyst rockstarContinue reading on Towards Data Science » Causal inference can help you become a business analyst rockstarContinue reading on Towards Data Science » FOLLOW US ON GOOGLE NEWS Read original article here Denial of responsibility! Techno Blender is an automatic aggregator of the all world’s media. In each content, the hyperlink to the primary source is specified. All trademarks belong to their rightful owners, all materials to their authors. If you…

A step-by-step guide in understanding the concept of dependence and how to apply it to validate directed acyclic graphs in causal inferenceContinue reading on Towards Data Science » A step-by-step guide in understanding the concept of dependence and how to apply it to validate directed acyclic graphs in causal inferenceContinue reading on Towards Data Science » FOLLOW US ON GOOGLE NEWS Read original article here Denial of responsibility! Techno Blender is an automatic aggregator of the all world’s media. In…

Inferring causal relationships between objects based on time series data is a significant problem that has been extensively studied in social and natural sciences across various fields. Credit: Institute for Basic Science In the quest to unravel the underlying mechanisms of natural systems, accurately identifying causal interactions is of paramount importance. Leveraging the advancements in time-series data collection through…

by Jo-An Occhipinti, Adam Skinner, Ian Hickie and Yun Ju Christine Song, The Conversation Credit: The Conversation Studies using traditional statistical methods have long indicated a link between unemployment and suicide. But until now it has been unclear if this relationship is causal. That is, even though the suicide rate is higher among the unemployed, can we definitely…

A step-by-step explanation of Directed Acyclic Graphs from the basics through to more advanced aspectsPhoto by Caleb Jones on UnsplashHaving spent a lot of time researching causal inference I began to realise that I did not have a full grasp of Directed Acyclic Graphs (DAGs) and that this was hampering my efforts to develop my understanding to a point where I could apply it in order to solve real-world problems.This objective of this article is to document my learning journey and to share everything you need to know about…

Test Effectiveness of any Treatment over Time with PCAPhoto by Raul Petri on UnsplashThe standard, presented in the literature and adopted at large scale by companies, to study the causal impacts of business actions (like design change, discount offers, and clinical trials) is for sure AB testing. When carrying out an AB test, we are doing a randomized experiment. In other words, we randomly split a population under our control (patients, users, customers) into two sets: a treatment and a control group. The treatment…

Understanding the identifying assumptions in experimentsPhoto by takaharu SAWA on UnsplashCausal inference without assumptions is impossible. Every available method requires untestable assumptions to establish causality from observed associations in the data. As such, stating the identifying assumptions and defending them is the most critical aspect of causal inference, yet it is also the most neglected one.In my previous article we kicked off the discussion around this topic by describing what identification is and why…