Advanced causal inference — Quasi-experimental designs and inference without ignorability

Introduction

What this guide covers

Introduction Advanced Statistics & Econometrics

Overview

Topics covered

• Introduction
• Causal Inference in Data Science: Quasi-experimental Designs and Models without Ignorability
• The Potential Outcomes Framework and the Holy Grail of Causality
• 1.1 The Stable Unit Treatment Value Assumption (SUTVA)
• 1.2 Population Causal Estimates
• Ignorability, Selection by Observables and Selection Bias

Related concepts

Main connections

• Causal inference & potential outcomes
• Calculus & differentiation
• Confounding & selection bias
• Linear regression
• Mean & expected value
• Meta-analysis & heterogeneity

Compatible notation

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