Guides
Causal Diagrams (DAGs)
13 free guides in this category.
Guides in this category.
Collider Bias & Selection Bias
Discover how conditioning on a common outcome can create false correlations and distort your view of reality.
Read guideCommon Causes (Confounders)
Understand how a hidden third factor can create a misleading link between two unrelated things.
Read guideCommon DAG Mistakes
Avoidable pitfalls when drawing and interpreting causal diagrams that can lead to biased conclusions.
Read guideCorrelation vs. Causation
Just because two things happen together doesn't mean one causes the other—this critical distinction helps you avoid being misled by coincidences in data.
Read guideDAGs and Regression Models
Use causal diagrams (DAGs) as a blueprint to build more accurate and less biased statistical regression models.
Read guideFeedback Loops and Cycles
Learn to identify and map the chains of cause-and-effect that amplify or stabilize systems, leading to better predictions and interventions.
Read guideM-Bias
A subtle form of bias that occurs when you mistakenly control for a variable that appears to be a confounder but is actually a collider, opening spurious causal pathways.
Read guideMeasurement Error
When the data we measure differs from reality, measurement error in our causal diagrams can hide, create, or reverse relationships between variables.
Read guidePrediction vs. Causation in DAGs
DAGs can predict outcomes from patterns, but they cannot prove that one thing causes another without additional assumptions about how variables truly relate.
Read guideStructural Causal Models (SCMs)
A powerful framework that combines diagrams and simple rules to represent causal relationships and predict the effects of interventions.
Read guideTesting Causal Assumptions
Methods for verifying whether causal model assumptions hold in observational data, including conditional independence tests and falsification checks.
Read guideCausal Sufficiency Assumption
The critical assumption that all common causes influencing both a treatment and an outcome have been measured and included in your analysis.
Read guidePositivity Assumption
The requirement that every individual must have had some chance of receiving each treatment option for valid causal comparison.
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