Applied Causal Inference Powered by ML and AI

TL;DR

This paper introduces the integration of machine learning and AI techniques into causal inference, emphasizing structural models and modern inference methods for improved causal analysis.

Contribution

It presents a comprehensive overview of classical and modern causal models, and discusses advanced ML-based inference methods like Double/Debiased Machine Learning.

Findings

Fusion of ML and causal inference enhances causal analysis capabilities

Modern inference methods improve accuracy in causal effect estimation

Structural models provide a robust framework for causal reasoning

Abstract

An introduction to the emerging fusion of machine learning and causal inference. The book presents ideas from classical structural equation models (SEMs) and their modern AI equivalent, directed acyclical graphs (DAGs) and structural causal models (SCMs), and covers Double/Debiased Machine Learning methods to do inference in such models using modern predictive tools.