Machine learning

As explained in Causal Inference for Data Science

Machine learning is a field of methods that enables computers to learn patterns or predictive relationships from data.

Machine learning learns predictive relationships from historical data, mapping input variables (x) to outcomes (y). Supervised learning commonly forecasts future sales or identifies spam emails. section 4.0

Prediction and causation answer different questions. Machine learning can predict illness successfully from correlated information, such as hospital-visit frequency, even when important comorbidities are missing; causal inference cannot reliably identify causes without relevant variables. section 1.1.2 section 4.0

Use machine learning with these checks:

• Treat accurate prediction as dependent on the future behaving like the past. section 4.6
• Do not treat model accuracy or cross-validation alone as evidence of causation. section 4.0 section 4.6
• With many confounders, use machine learning inside the adjustment formula and apply cross-fitting to reduce over-fitting. section 4.6

Applying the adjustment formula

Trace how machine learning models can supply conditional probabilities within the adjustment formula. section 4.0

How Causal Inference for Data Science covers it

How deep it goes

Defines supervised prediction, contrasts it with causation, and reaches adjustment formulas and cross-fitting without deriving model internals.

Examples

Uses illness prediction, hospital-visit frequency, sales forecasting, spam detection, image recognition, language translation, and autonomous vehicles.

What you can do after

Evaluate when predictive models fit a task, recognize causal limitations, and combine models with adjustment formulas when confounders are numerous.

How it connects

Links machine learning to supervised learning, causal inference, confounding variables, adjustment formulas, cross-validation, and cross-fitting.

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Related concepts

• artificial intelligencekind of · Artificial intelligence is the field concerned with creating computational systems that perform tasks associated with human intelligence, such as learning, reasoning, perception, or decision-making.
• language modelincludes · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
• supervised learningincludes · Supervised learning is a machine-learning approach that learns to map inputs to target outputs from examples paired with known labels or values.
• reinforcement learningincludes · Reinforcement learning is a machine-learning paradigm in which an agent learns actions or a policy through interaction with an environment and evaluative feedback, such as rewards.
• deep learningincludes · Deep learning is a machine-learning approach that uses neural networks with multiple computational layers to learn representations from data.
• datauses · Data is represented information, observations, measurements, or other values that can be stored, processed, transmitted, analyzed, or interpreted.

This explainer was generated by Manning's knowledge graph from the text of Causal Inference for Data Science. The section links point to the parts of the book it draws on.