Causal Inference

Overview

Causal inference determines cause-and-effect relationships and estimates treatment effects, going beyond correlation to understand what causes what.

When to Use

• Evaluating the impact of policy interventions or business decisions
• Estimating treatment effects when randomized experiments aren't feasible
• Controlling for confounding variables in observational data
• Determining if a marketing campaign or product change caused an outcome
• Analyzing heterogeneous treatment effects across different user segments
• Making causal claims from non-experimental data using propensity scores or instrumental variables

Key Concepts

• Treatment: Intervention or exposure
• Outcome: Result or consequence