Causal inference
Causal inference is the process of determining or estimating how causes, exposures, or interventions affect outcomes, using data and assumptions about causal relationships.
Causal Inference for Data Science
A causal problem is a problem of determining whether and how one factor causes an outcome.
- How it is used
- Guides decisions about changing treatments, policies, products, and other factors when the goal is to affect outcomes.
- How deep it goes
- Develops workflows, graphs, confounding, ATE, and estimation methods while deferring detailed formulas and later graph tools.
- Examples
- Uses illness treatments, website traffic, arcade revenue, COVID-19 triage, and synthetic game-level data to expose misleading associations.
- What you can do after
- Evaluate/understand whether a causal question needs an experiment or observational analysis, then map confounders and assumptions before estimating effects.
Read how Causal Inference for Data Science explains causal inference →
Causal AI
A method for estimating or reasoning about the causal effects of interventions or exposures from data.
- How it is used
- Turns intervention and explanation questions into data-supported analyses for experiments, observational studies, and decisions.
- How deep it goes
- Develops a query-to-refutation workflow and causal effect estimation, while treating other causal queries as extensions.
- Examples
- Uses an online-game engagement experiment, an e-commerce feature’s effect on sales, and handwritten-digit images.
- What you can do after
- Apply it via DoWhy to estimate causal effects while checking whether assumptions fit the problem.
Related concepts
- language modeluses · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
- observational datauses · Observational data are measurements collected without the investigator assigning or controlling the conditions, treatments, or interventions being studied.
- cross-validationcontrasts with · Cross-validation is a resampling technique that evaluates a model by repeatedly fitting it on some portions of available data and assessing it on held-out portions.
- causationimplemented by · Causation is a relationship in which one event, condition, or factor produces or influences a change in another.
- rule-based systemuses · A rule-based system produces outputs or actions by applying explicitly specified rules or conditions to inputs or represented facts.
- treatment effectproduces · A treatment effect is the difference in an outcome attributable to receiving an intervention or exposure rather than a comparison condition.
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