Propensity score
A propensity score is the estimated conditional probability that a unit receives a particular treatment or action given observed covariates or context.
Causal Inference for Data Science
A propensity score is the conditional probability that a unit receives a treatment given its observed covariates.
- How it is used
- Uses treatment-assignment probabilities to find comparable observational cases, assess positivity, and support ATE estimation.
- How deep it goes
- Defines the score, explains grouping and overlap, and practices machine-learning estimation with cross-fitting; defers specialized theory.
- Examples
- Uses age-based patient groups, a surgical-treatment scenario, and Right Heart Catheterization survival data.
- What you can do after
- Apply it via logistic regression or other classifiers to inspect overlap and estimate effects for comparable populations.
Read how Causal Inference for Data Science explains propensity score →
Causal AI
A propensity score is a function of observed covariates representing the probability or treatment-predictive summary associated with receiving a treatment.
- How it is used
- Uses treatment-predictive summaries to adjust for confounders when estimating backdoor causal effects.
- How deep it goes
- Defines probability and function forms, explains dimensionality reduction, and names matching, stratification, and weighting methods.
- Examples
- Uses an online gaming example involving engagement, guild membership, play time, and player skill.
- What you can do after
- Apply it via DoWhy using propensity-score stratification, matching, or inverse-probability weighting for ATE estimation.
Related concepts
- positivity assumptionuses · The positivity assumption is the condition that every treatment has a nonzero and non-unit probability of being assigned at every confounder value that occurs in the population.
- average treatment effect estimationuses · Average treatment effect estimation quantifies the population-average difference in outcomes that would result from two alternative treatments or interventions.
- propensity score methodused by · A propensity-score method is a causal-inference technique that estimates treatment effects by matching, weighting, stratifying, or otherwise adjusting units according to their observed treatment probabilities.
- machine learninguses · Machine learning is a field of computational methods in which systems learn patterns or relationships from data to make predictions, decisions, or other outputs.
- causal adjustmentused by · Causal adjustment estimates a causal effect by conditioning on variables that block noncausal pathways and averaging over their target-population distribution.
- backdoor pathaddresses · A backdoor path is a path between a putative cause and an outcome that begins with an arrow into the cause and can create a noncausal association.
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