Causal · PSM
Propensity Score Matching
Estimate treatment effects by matching treated and control units on propensity scores. Includes balance check, love plot, and ATT estimation.
Step 1 — Enter your data
Format: first column = treatment (1/0), second column = outcome, remaining columns = covariates. First row = headers.
Propensity Score Model
Logistic regression — propensity scores
Matching Results
Matched sample summary
Balance Check
Standardised mean differences (SMD) before and after matching
| Covariate | SMD Before | SMD After | Balanced? |
|---|
Love Plot
Covariate balance visualisation
Treatment Effect Estimation
Average Treatment Effect on the Treated (ATT)
Plain English interpretation
Learn more — related lessons on rubiX
Propensity score model
Nearest neighbour matching
Balance check & love plots
Treatment effect estimation (ATT & ATE)
Causal inference foundations