Statistics and experimental design · MLD-0153
Causal inference
Estimating effects of interventions using assumptions, study design, and data.
Original reference page · detailed expansion pending. Original definitions, examples, notes and citations are preserved below. New examples are illustrative, not benchmark results.
Original example
Estimate the effect of changing a recommendation policy.
Original caveat
Causal conclusions require more than a predictive model.
Context and aliases
Level: Intermediate. Stages: Theory · Evaluation · Research.
Tags: ML · DL · SLM · LLM.
Aliases: None recorded.
Related concepts
Concepts linking here
Technology connections
- DGL — Framework for graph machine learning.
- Diffrax — Differential equation solving library for JAX.
- DoWhy — Causal inference framework.
- EconML — Machine-learning methods for heterogeneous treatment effects.
- GPyTorch — Gaussian-process modeling library in PyTorch.
- NetworkX — Python library for graph analysis.
- NumPyro — JAX-based probabilistic programming library.
- PyMC — Probabilistic programming library for Bayesian models.
- PyTorch Geometric — Graph neural-network library built on PyTorch.
Original references
- scikit-learn: cross-validation ↗ · Reference link
- Mathematics for Machine Learning ↗ · Reference link
- scikit-learn: evaluation metrics ↗ · Reference link
- Original reference URL ↗