Modern causal inference and machine learning — Expert reference

Introduction

What this guide covers

A comprehensive and technical treatise on the unification of structural causal modeling, Rubin's theory of potential outcomes, and machine learning algorithms for the estimation of heterogeneous effects.

Overview

Topics covered

• Introduction
• Modern Causal Inference and Machine Learning
• Potential Outcomes Theory and Causal Foundations
• Critical Assumptions for Identification
• Simpson's Paradox and Confusion Bias
• Structural Causal Models (SCM) and DAGs

Related concepts

Main connections

• Causal inference & potential outcomes
• Directed acyclic graphs (DAGs)
• Confounding & selection bias
• Mean & expected value
• Residual analysis & diagnostics
• Linear regression

Compatible notation

Mathematical sample in LaTeX

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