Causal Inference glossary
49 causal inference terms, each defined in plain language with what the number hides once real ad spend runs through it. Definitions are written for ecommerce operators, not statisticians.
Causal Inference terms
- Association vs. CausationAssociation indicates a relationship between two variables. Causation means a change in one variable directly produces a change in another.
- Attrition BiasAttrition Bias occurs when participants who leave a study or marketing funnel differ systematically from those who remain. This skews results and…
- Average Treatment Effect (ATE)The average treatment effect (ATE) is the average causal effect of a treatment over a whole population: the mean of each unit's outcome with…
- Average Treatment Effect on the Treated (ATT)The average treatment effect on the treated (ATT) is the average causal effect of a treatment among the units that actually received it. It is the…
- Backdoor CriterionBackdoor Criterion identifies a sufficient set of variables to control for confounding between a treatment and an outcome. It blocks all 'backdoor…
- BlindingBlinding is a procedure where one or more parties in an experiment do not know which treatment subjects received. It prevents bias in study results.
- Causal ChainA Causal Chain is a sequence of events where each event causes the next, leading from an initial cause to a final effect.
- Causal DiscoveryCausal Discovery infers causal relationships from data, using statistical methods and machine learning to uncover a system's causal structure.
- Causal ForestsCausal forests are a random-forest method that estimates how a treatment's effect differs between units with different characteristics. Its target is…
- Causal ModelA Causal Model is a mathematical representation describing the causal relationships between variables, used to reason about and estimate intervention…
- Causal PathwayA Causal Pathway is the sequence of events through which a cause produces an effect.
- CausalityCausality is the relationship where one event directly causes another, essential for identifying specific actions that drive desired outcomes in…
- ColliderA Collider is a variable in a directed acyclic graph (DAG) that two or more other variables cause. Conditioning on a collider opens a non-causal path…
- ConfoundingConfounding is a distortion of the estimated treatment effect when a third variable, a confounder, associates with both the treatment and the…
- CounterfactualA counterfactual is what would have happened under conditions that did not occur, such as a customer's outcome had they not received the campaign…
- d-separationd-separation is a graphical criterion that determines conditional independence between sets of variables in a Directed Acyclic Graph (DAG). It…
- Directed Acyclic Graph (DAG)Directed Acyclic Graph (DAG) is a graphical representation of causal relationships between variables. Nodes represent variables, and directed edges…
- Do-CalculusDo-Calculus is a set of rules for manipulating probability distributions to estimate the causal effect of an intervention from observational data.
- Double Machine LearningDouble Machine Learning is a statistical method for estimating causal parameters when high-dimensional confounding exists.
- EconometricsEconometrics applies statistical methods to economic data to provide empirical content to economic relationships. It provides tools for causal…
- ExogeneityExogeneity is the assumption that an explanatory variable is uncorrelated with a model's error term, the part of the outcome the model leaves…
- ExperimentsExperiments are scientific procedures that test hypotheses or demonstrate facts. In marketing, experiments like A/B tests determine the causal effect…
- Frontdoor CriterionThe front-door criterion identifies the effect of a treatment on an outcome through a mediator, even when an unmeasured common cause of the two…
- g-computationg-computation is a method for estimating the causal effect of a time-varying treatment from longitudinal data. It uses a parametric model to estimate…
- Hawthorne EffectHawthorne Effect is a type of reactivity where individuals modify their behavior because they know they are being observed.
- Heterogeneous Treatment EffectsHeterogeneous treatment effects are variations in a treatment's causal impact across different population subgroups. Understanding these effects is…
- Immortal Time BiasImmortal Time Bias occurs in observational studies when the follow-up period includes time where the outcome cannot happen. This inflates the…
- Interaction EffectAn Interaction Effect occurs when one variable's effect on an outcome depends on another variable's level.
- Interrupted Time Series (ITS)Interrupted time series (ITS) is a quasi-experimental design that tracks an outcome before and after a known intervention and tests for a change in…
- InterventionAn Intervention is an action taken to produce a change in an outcome.
- Inverse Probability Weighting (IPW)Inverse Probability Weighting (IPW) is a statistical method that estimates causal effects from observational data by weighting subjects based on…
- Local Average Treatment Effect (LATE)Local Average Treatment Effect (LATE) is the average causal effect of a treatment on individuals whose treatment status changes due to an…
- Marginal Structural Model (MSM)A Marginal Structural Model (MSM) is a statistical model that estimates the causal effect of a time-varying treatment from longitudinal data. It uses…
- MatchingMatching estimates a treatment effect from observational data by comparing treated units with untreated units that look alike on chosen variables. It…
- Mediation AnalysisMediation analysis is a statistical method that explains how a treatment affects an outcome. It separates direct effects from indirect effects…
- Natural ExperimentNatural Experiment is an empirical study where experimental and control conditions are determined by nature or external factors. This estimates…
- Observational StudyObservational Study observes the effects of a treatment or intervention without controlling exposure. It does not use randomization, making it…
- Placebo EffectThe Placebo Effect is a beneficial outcome from a treatment attributed to a patient's belief, not the treatment itself. In marketing, customer…
- Potential Outcomes FrameworkThe potential outcomes framework defines a causal effect as the difference between the outcome a unit would have with a treatment and the outcome it…
- Quasi-ExperimentA quasi-experiment estimates the causal impact of an intervention without random assignment. It applies when random assignment is not feasible or…
- RandomizationRandomization: The process of assigning subjects to treatment and control groups by chance. This minimizes confounding and selection bias, allowing…
- Randomized Controlled Trial (RCT)Randomized Controlled Trial (RCT): A study design where subjects are randomly assigned to a treatment or control group. RCTs are the standard for…
- Regression to the MeanRegression to the Mean describes the phenomenon where an extreme variable measurement tends to be closer to the average on subsequent measurements…
- Simpson's ParadoxSimpson's Paradox shows a trend in data that reverses when groups combine. It proves association does not equal causation.
- Spurious CorrelationSpurious Correlation is a statistical relationship between variables that are not causally linked. It occurs due to coincidence or an unobserved…
- Structural Causal Model (SCM)A structural causal model describes a system as a set of equations, one per variable, each giving the variable's value from its direct causes and…
- Survivorship BiasSurvivorship bias is the logical error of focusing on successful outcomes while ignoring failures. This leads to false conclusions by overlooking…
- Synthetic Control MethodThe Synthetic Control Method estimates the causal effect of an intervention in a single case study. It constructs a 'synthetic' control unit from a…
- Treatment EffectTreatment Effect is the causal impact of an intervention on an outcome. In marketing, this means the change in a metric like conversion rate directly…
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