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Causal Inference

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Determine cause-and-effect relationships using propensity scoring, instrumental variables, and causal graphs for policy evaluation and treatment effects

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# 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
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