Causal AI in a Nutshell

Distinguish traditional AI from causal AI by focusing on cause and effect and predicting decision impact. Explore causal graphs from association to causation and address challenges in a lightweight way.

Causal graphs use nodes and arrows to depict cause-and-effect relationships among variables, showing how changing a variable may influence others and the difference between association and causation for decisions.

Explore how causal graphs distinguish causation from association and bias, using umbrellas and ice cream examples to guide decision making and bias elimination.

Use causal graphs to identify confounding and collider bias, and remove bias by conditioning on job type, revealing the causal link between smoking and lung cancer.

Discover how to create causal graphs using causal discovery, combining domain knowledge with causal discovery algorithms to build accurate graphs from data and address subjectivity in domain knowledge.

Examine a treatment A versus B example to show how initial health condition biases estimates, then compute unbiased effects within initial condition subgroups and discuss AI models for treatment choice.

Explore the main challenges of causal AI, including uncertain causal graphs, biased associations, and the difficulty of validating causal effects without definitive truth across the ladder of causation.

Explore how causal graphs reveal cause and effect, identify biases from confounders and colliders, and condition appropriately to distinguish association from causation with artificial intelligence models.

Explore how cognitive science informs causal ai projects, compare it with traditional ai, and map real-world project steps, team roles, and handy tools.

Define the business question with stakeholders and build a causal graph. Select modeling techniques, remove biases, ensure data quality, and simulate what-if scenarios to optimize decisions.

Identify the key roles in a causal ai team—business stakeholders, domain experts, data engineers, and data scientists—who guide strategy, provide context, prepare data, and build trustworthy causal graphs.

Explore open source Python libraries for causal inference—do ai, ml, and causal learn—and a paid platform for large-scale deployment across departments.

Explore a quick, thorough introduction to causal AI and its differences from standard AI. Follow recommended courses and books to learn estimating causal effects from observational data.