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The 132 pages that link to Causal inference, each with the reason it gives.

• BiostatisticsRelated: Biomedical decisions often require causal effects, not merely observed associations.

• Ceteris paribusRelated: Causal claims require separating an effect from changes in other relevant conditions.

• Econometric modelRelated: Econometric models can estimate causal effects when their identifying assumptions hold.

• Econometric SocietyRelated: It captures a central modern challenge for empirical economics and econometric research.

• MulticollinearityRelated: Whether to retain correlated causes depends on the causal question, not just coefficient precision.

• Data scienceRelated: It addresses questions that prediction alone cannot answer.

• Molecular epidemiologyRelated: Molecular associations alone do not establish that a biological factor caused disease.

• Data analysisRelated: It separates causal questions from descriptive patterns and associations.

• Mathematical statisticsRelated: Its estimands and identification assumptions extend statistical inference beyond association.

• News analysisRelated: Claims about why an event happened require more than noting that factors occurred together.

• Nonparametric statisticsRelated: Flexible nuisance estimates can help causal analyses, but do not replace identification assumptions.

• CliometricsRelated: Historical studies use causal designs to distinguish effects from coincidence.

• Race and intelligenceRelated: Group comparisons require causal reasoning to distinguish ancestry from social exposures and confounders.

• Economic methodologyRelated: It sharpens the methodological question of when economic evidence supports causal claims.

• Educational researchRelated: Educational research often asks whether policies or teaching practices cause changes in outcomes.

• Vacation rentalRelated: It is needed to separate rental effects on rents from wider housing-market changes.

• Computational biologyRelated: Predictive associations from biological data do not by themselves establish causal mechanisms.

• Repetitive strain injuryRelated: Studies must distinguish workplace causes from personal, medical, and nonwork influences.

• Abhijit BanerjeeRelated: His field experiments aim to identify the effects of specific development programs.

• InductivismRelated: Moving from observed associations to causal claims is a demanding form of scientific generalization.

• Programme for International Student AssessmentRelated: PISA comparisons are observational and cannot by themselves show that a policy caused score differences.

• Research methodologyRelated: Causal claims require designs and assumptions beyond simple association.

• CliodynamicsRelated: Historical correlations alone cannot establish the causal mechanisms cliodynamic models propose.

• James HeckmanRelated: His work made selection and identification central concerns in empirical policy research.

• Peltzman effectRelated: It helps distinguish behavioral responses from other changes accompanying safety regulation.

• Positive economicsRelated: It helps distinguish causal economic explanations from mere observed correlations.

• Ramón Villeda MoralesRelated: Separating a blood factor’s causal action from correlated age changes is a central experimental challenge.

• Statistical analysisRelated: Association alone does not establish the causal claims some analyses seek.

• Advertising researchRelated: Establishing that advertising caused a change is harder than observing that they occurred together.

• Behavioural sciencesRelated: It addresses the gap between behavioral associations and causal explanations.

• Chemically induced disordersRelated: A chemical’s presence alongside illness does not by itself prove it caused the disorder.

• Curie's principleRelated: Curie’s principle concerns causal structure, but does not by itself establish empirical causation.

• Folie à deuxRelated: It is difficult to separate interpersonal influence from shared circumstances or prior vulnerability.

• Gaëtan DugasRelated: A position in a contact network cannot alone establish that someone caused an epidemic.

• Kenneth and Mamie ClarkRelated: The studies informed arguments about segregation’s harms, while causal claims require careful interpretation.

• Scientific modellingRelated: Causal models distinguish proposed mechanisms from mere patterns in data.