Book V

Causal inference

DAGs, interventions, counterfactuals, perturbation

Move from prediction to intervention: what a causal claim is, when data can support one, and when it cannot.

9 chapters0 propositions written

Chapter ICorrelation and CausationWhy prediction is not enough.Correlation · Causation · Confounding · Prediction versus interventionnot yet written
Chapter IICausal GraphsDrawing the assumptions you cannot test.DAGs · Nodes · Edges · Paths · Ancestors · Descendants · Colliders · Confoundersnot yet written
Chapter IIIInterventionsThe do-operator.Do-operator · Intervention · Observational distribution · Interventional distributionnot yet written
Chapter IVIdentificationWhen the data can answer the question at all.Backdoor criterion · Adjustment · Front-door criterion · Conditional independencenot yet written
Chapter VCounterfactualsWhat would have happened instead.Potential outcomes · Counterfactual worlds · Individual treatment effects · Causal effectnot yet written
Chapter VIExperimentsRandomisation, and why it works.Randomisation · Treatment and control · A/B testing · Experimental design · Power · Biasnot yet written
Chapter VIIObservational StudiesCausal claims without an experiment.Propensity scores · Matching · Inverse probability weighting · Regression adjustment · Sensitivity analysisnot yet written
Chapter VIIICausal MLWhere machine learning meets the do-operator.Heterogeneous treatment effects · Causal forests · Representation learning · Neural causal models · Causal discoverynot yet written
Chapter IXCausal Reasoning in AIWhere this reaches the rest of the corpus.Machine learning · Vision · Language · Scientific discovery · Healthcarenot yet written