# Recommender Systems & Causal Inference

Collaborative filtering, content-based methods, neural CF, causal inference, DiD, and A/B testing.

Use this subtrack when you want product-facing experimentation and decision systems rather than generic supervised-learning practice. It fits especially well for marketplace, personalization, and growth-oriented problems.

## How To Use This Subtrack Well

- Separate recommendation quality questions from causal-impact questions instead of treating them as the same problem.
- Build one recommender baseline and one experiment-analysis workflow before adding complexity.
- Pair this work with [../../27-causal-inference/README.md](../../27-causal-inference/README.md) if the experimentation side needs deeper conceptual support.

## What Comes Next

- Continue to [../README.md](../README.md) for the broader practical data science roadmap.
- Continue to [../../27-causal-inference/README.md](../../27-causal-inference/README.md) for deeper effect-estimation concepts.
- Continue to [../../16-model-evaluation/README.md](../../16-model-evaluation/README.md) if you want stronger experimental reporting and comparison habits.
