# Computational Causal Inference for Applied Researchers

**A Practical Guide for Epidemiologists and Biostatisticians**

**Authors:** Miguel Angel Luque-Fernandez & Matthew J. Smith

A Quarto book introducing causal inference from a beginner's perspective, leading to advanced computational methods using **R** and **Stata**.

## Chapters

| Part | Chapter | Topic |
|------|---------|-------|
| I: Foundations | 1 | Introduction to Causal Inference |
| | 2 | Regression Adjustment |
| II: G-Methods | 3 | The G-Formula |
| | 4 | Propensity Score Methods |
| III: Advanced Methods | 5 | Double-Robust Estimators (AIPW, TMLE) |
| | 6 | Causal Inference for Longitudinal Data |
| | 7 | Mediation Analysis |
| IV: Sensitivity & Discussion | 8 | Sensitivity Analysis |
| | 9 | Discussion |

## Features

- ~65 R code listings with reproducible examples
- Stata implementations for key methods
- Theorem, definition, example, and proof callout environments
- Per-chapter glossaries defining key terms
- APA-formatted bibliography with 700+ references
- UGR-branded visual theme

## Build

```bash
quarto render
```

Output lands in `docs/`.

## License

MIT — see [LICENSE](LICENSE).