KnowraCausal inferenceLinked fromLinked fromThe 132 pages that link to Causal inference, each with the reason it gives.All 132Broader topic 1Related 86Narrower topic 28Compared with 17Randomized 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.ConfoundingNarrower topic: Confounding is a central obstacle to identifying causal effects.Medication adherenceNarrower topic: Separating adherence effects from patient and treatment differences requires causal reasoning.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.Longitudinal studyNarrower topic: Longitudinal evidence helps establish sequence but alone does not prove causation.Historical causationNarrower topic: It supplies general tools for distinguishing causal effects from mere association.Natural experimentNarrower topic: Natural experiments use observational variation to estimate causal effects.Koch's postulatesNarrower topic: The postulates are an early experimental framework for inferring microbial causation.RandomizationNarrower topic: Randomization is a design strategy for identifying causal effects.Climate change attributionNarrower topic: Climate attribution adapts causal reasoning to a system with interacting drivers and limited experiments.Quasi-experimentNarrower topic: Quasi-experiments are one family of designs for estimating causal effects.Program evaluationNarrower topic: It explains the assumptions behind claims that a program produced observed effects.Instrumental variableNarrower topic: Instrumental variables are one strategy for identifying effects when direct adjustment is inadequate.AdditionalityNarrower topic: It supplies methods for distinguishing intervention effects from background changes.Control groupNarrower topic: A control group supports causal estimates by approximating what would happen without the intervention.Experimental methodNarrower topic: Experiments are designed to support causal conclusions, not merely describe associations.Experimental economicsNarrower topic: Experimental economics uses controlled variation to strengthen causal conclusions.Suicide contagionNarrower topic: Exposure and suicidal behavior may coincide without exposure causing the change.Economics of educationNarrower topic: Estimating education’s effects requires separating schooling from the traits and circumstances that influence it.Trygve HaavelmoNarrower topic: Haavelmo’s structural perspective links economic equations to claims about causal relationships.Regression toward the meanNarrower topic: A before-and-after improvement alone cannot show that a treatment caused the change.Austin Bradford HillNarrower topic: Hill’s considerations are one influential approach to causal inference from observational associations.Correlation does not imply causationNarrower topic: It develops tools for moving beyond association toward justified causal conclusions.Michael KremerNarrower topic: Kremer’s experiments use causal reasoning to distinguish program effects from correlation.
KnowraCausal inferenceLinked fromLinked fromThe 132 pages that link to Causal inference, each with the reason it gives.All 132Broader topic 1Related 86Narrower topic 28Compared with 17Randomized 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.ConfoundingNarrower topic: Confounding is a central obstacle to identifying causal effects.Medication adherenceNarrower topic: Separating adherence effects from patient and treatment differences requires causal reasoning.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.Longitudinal studyNarrower topic: Longitudinal evidence helps establish sequence but alone does not prove causation.Historical causationNarrower topic: It supplies general tools for distinguishing causal effects from mere association.Natural experimentNarrower topic: Natural experiments use observational variation to estimate causal effects.Koch's postulatesNarrower topic: The postulates are an early experimental framework for inferring microbial causation.RandomizationNarrower topic: Randomization is a design strategy for identifying causal effects.Climate change attributionNarrower topic: Climate attribution adapts causal reasoning to a system with interacting drivers and limited experiments.Quasi-experimentNarrower topic: Quasi-experiments are one family of designs for estimating causal effects.Program evaluationNarrower topic: It explains the assumptions behind claims that a program produced observed effects.Instrumental variableNarrower topic: Instrumental variables are one strategy for identifying effects when direct adjustment is inadequate.AdditionalityNarrower topic: It supplies methods for distinguishing intervention effects from background changes.Control groupNarrower topic: A control group supports causal estimates by approximating what would happen without the intervention.Experimental methodNarrower topic: Experiments are designed to support causal conclusions, not merely describe associations.Experimental economicsNarrower topic: Experimental economics uses controlled variation to strengthen causal conclusions.Suicide contagionNarrower topic: Exposure and suicidal behavior may coincide without exposure causing the change.Economics of educationNarrower topic: Estimating education’s effects requires separating schooling from the traits and circumstances that influence it.Trygve HaavelmoNarrower topic: Haavelmo’s structural perspective links economic equations to claims about causal relationships.Regression toward the meanNarrower topic: A before-and-after improvement alone cannot show that a treatment caused the change.Austin Bradford HillNarrower topic: Hill’s considerations are one influential approach to causal inference from observational associations.Correlation does not imply causationNarrower topic: It develops tools for moving beyond association toward justified causal conclusions.Michael KremerNarrower topic: Kremer’s experiments use causal reasoning to distinguish program effects from correlation.