## Causal Inference in Statistics: A PrimerMany of the concepts and terminology surrounding modern causal inference can be quite intimidating to the novice. Judea Pearl presents a book ideal for beginners in statistics, providing a comprehensive introduction to the field of causality. Examples from classical statistics are presented throughout to demonstrate the need for causality in resolving decision-making dilemmas posed by data. Causal methods are also compared to traditional statistical methods, whilst questions are provided at the end of each section to aid student learning. |

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Causal Inference in Statistics: A Primer Judea Pearl,Madelyn Glymour,Nicholas P. Jewell Limited preview - 2016 |

Causal Inference in Statistics: A Primer Judea Pearl,Madelyn Glymour,Nicholas P. Jewell Limited preview - 2016 |

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adjustment formula analysis answer assume assumptions backdoor criterion backdoor paths Bareinboim Bayes block blood pressure cancer causal effect causal graph Causal Inference causal model coin flip collider compute conditional independence conditional probabilities correlation counterfactual covariates d-separation data set dependent determine direct effect directed graph do-expressions do-operator do(X Door error terms estimate event example expected value expression freeway function gender given graphical model representing hiring homework hypothetical identifiable instrumental variables intervention joint distribution Judea Pearl lumpectomy lung cancer measured mediation model in Figure Monty Monty Hall problem nodes observed obtain outcome P(yldo(x parameters path coefficient patients Pearl population predict probability distribution problem questions Study question random recovery regression coefficients regression equation relationships samples satisfies the backdoor Section set of variables Simpson's paradox smoking specific statistical structural equations Study questions Study Table take the drug Theorem total effect treatment X P(Y