This is a broad tutorial covering basics of graphical and potential outcome frameworks, conditioning and natural experiment-based methods, sensitivity analyis, ...

10 сент. 2020 г. · The goal of this tutorial is to introduce central concepts, algorithms, and techniques of causal inference for a machine learning audience.

In this tutorial, we will present a comprehensive overview of the intersection of causal inference and machine learning.

This tutorial presents a walk-through on using DoWhy+EconML libraries for causal inference. Along the way, we'll highlight the connections to machine learning— ...

We will introduce causal inference frameworks and methods that are needed for the design and analysis of big real-world observational data and pragmatic trials.

This tutorial will introduce key concepts in machine learning-based causal inference. It's an ongoing project and new chapters will be uploaded as we finish ...

In this tutorial, we will introduce both traditional and state-of-the-art representation learning algorithms for treatment effect estimation. Background about ...

Introduction to Causal Inference. A free online course on causal inference from a machine learning perspective.

In this article, I am going to tell you what causal inference is, why you should care, how you can use causal inference for your work, the methods, and lastly ...

To make a causal inference from statistics, the equation (function form) must be stable under intervention (i.e., variables are manipulated). Statistics is used ...