Causal Inference: Fitting Methods for Observational Data

Introduction

What this guide covers

Introduction Introduction Data Science and Biostatistics

Overview

Topics covered

• Introduction
• The Potential Outcomes Paradigm and Acyclic Directed Graphs
• The Neyman-Rubin Potential Outcomes Framework
• Fundamental Assumptions of Identifiability
• Causal Structural Models and DAGs (Judea Pearl)
• Fundamental Graphic Structures and d-separation

Related concepts

Main connections

• Causal inference & potential outcomes
• Variance & standard deviation
• Directed acyclic graphs (DAGs)
• Calculus & differentiation
• Confidence interval
• Bootstrap resampling

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