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

• Randomized controlled trialNarrower topic: Randomized trials are a design for strengthening causal conclusions.

• Comparative methodNarrower topic: Comparative designs are one route to causal claims, not a guarantee of them.

• EpidemiologyRelated: Epidemiologic associations require causal reasoning before they justify prevention decisions.

• EpigeneticsRelated: Epigenetic differences can be consequences of disease or exposure rather than their causes.

• EmpiricismRelated: Empirical observations motivate causal claims, but correlation alone does not establish causation.

• CausalityRelated: It provides methods for drawing causal conclusions when direct intervention is limited.

• Machine learningCompared with: Predictive accuracy alone does not establish that a model has identified causal effects.

• Sensitivity analysisCompared with: Model sensitivity describes response within a model, not necessarily real-world causation.

• ConfoundingNarrower topic: Confounding is a central obstacle to identifying causal effects.

• Algorithmic biasRelated: Causal analysis can distinguish pathways that transmit historical disadvantage from mere statistical association.

• SociologyRelated: It separates social explanations from mere correlations between variables.

• EvidenceRelated: Causal claims require evidence that distinguishes causes from mere associations.

• Genome-wide association studyCompared with: Statistical association alone does not show that a variant causes the trait.

• Medication adherenceNarrower topic: Separating adherence effects from patient and treatment differences requires causal reasoning.

• ComorbidityRelated: Co-occurrence alone cannot show whether one condition caused another.

• Conditional probabilityRelated: Observational conditioning alone does not generally identify the effect of an intervention.

• InferenceBroader topic: Causal inference distinguishes causes from associations in observed data.

• Statistical inferenceRelated: It targets causal effects, a stronger claim than ordinary population description.

• Inductive reasoningRelated: Causal conclusions require more than observing that events or traits occur together.

• Linear regressionRelated: Regression can adjust for measured covariates, but alone does not establish causation.

• Experimental designNarrower topic: Experimental plans aim to support causal claims, not merely describe associations.

• External validityNarrower topic: External validity asks where a causal conclusion remains true.

• Selection biasNarrower topic: Selection bias threatens whether an observed association supports a causal conclusion.

• Field experimentNarrower topic: The central aim is to infer effects despite the complexity of real-world settings.

• Media effectsRelated: Separates media influence from selection effects and other explanations.

• Observational studyRelated: Observational evidence supports causal claims only when assumptions and alternative explanations are addressed.

• Regression analysisCompared with: A fitted regression association alone does not identify a causal effect.

• Longitudinal studyNarrower topic: Longitudinal evidence helps establish sequence but alone does not prove causation.

• ObservationCompared with: Observed associations alone generally do not establish that one event caused another.

• Critical thinkingRelated: It guards against treating correlation or sequence as proof of cause.

• A/B testingRelated: Randomized assignment supports causal conclusions when implementation and measurement are sound.

• Risk compensationRelated: Separating compensation from other behavioral changes is a central challenge.

• Scientific theoryRelated: Explanatory theories often propose causal relations that evidence must support.

• Single-nucleotide polymorphismRelated: A SNP-trait association may reflect linkage rather than a direct biological effect.

• RecidivismRelated: A lower measured rate after a program does not by itself prove the program caused the change.

• Bayesian networksRelated: Causal networks extend graph-based probability models with assumptions about intervention and direction.

• Gene regulatory networkRelated: Expression correlations alone cannot establish regulatory direction or causation.

• Genotype–phenotype correlationCompared with: A genotype–phenotype correlation alone does not establish that the genetic difference causes the trait.

• Historical causationNarrower topic: It supplies general tools for distinguishing causal effects from mere association.

• Intergenerational traumaRelated: It is difficult to distinguish biological inheritance from parenting, context, and selection effects.

• Natural experimentNarrower topic: Natural experiments use observational variation to estimate causal effects.

• StatisticsRelated: Association alone does not establish causation; causal claims require additional design or assumptions.

• EconometricsRelated: Econometric designs seek causal effects, not merely statistical associations.

• Evidence-based policyRelated: Policy choices often depend on whether observed outcomes can be attributed to an intervention.

• Historical accuracyRelated: Historical explanations can misstate the past when they confuse sequence with cause.

• Koch's postulatesNarrower topic: The postulates are an early experimental framework for inferring microbial causation.

• Neural circuitRelated: Perturbation experiments are needed to distinguish circuit causes from activity correlations.

• Quantum nonlocalityRelated: Bell's assumptions can be expressed as constraints on causal influence.

• RandomizationNarrower topic: Randomization is a design strategy for identifying causal effects.

• Self-fulfilling prophecyRelated: Establishing an expectancy effect requires separating expectation-driven change from correlation or selection.