Advanced Analytics · TOOL GUIDE

Propensity Score Matching

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Understand the method. Use it with confidence.

Reduce selection bias in observational data. Quantly runs the method against your active analysis context and returns structured results for review and interpretation.

01

When to use it

Use this tool when the question requires multivariate structure, segmentation, simulation, causal-design assumptions, or model explainability.
Use it only when the selected variables and study design match the method's requirements.

02

How to use it

  1. Open the tool from the Quantly Tools panel.
  2. Confirm the active dataset and analysis session.
  3. Set Outcome Variable (Required): The dependent variable to analyze.
  4. Set Treatment Variable (Binary) (Required): Binary variable indicating Treatment (1) vs Control (0).
  5. Set Covariates (Confounders) (Required): Variables to match on (that predict treatment assignment).
  6. Set Categorical Encoding (Required): How to transform text variables for the model. One-Hot is usually safer for this model.
  7. Review optional or advanced settings and keep defaults unless your design requires a change.
  8. Run the tool, inspect warnings and diagnostics, then save or export the result with its assumptions.
03

How to interpret results

Interpret results in the context of preprocessing, tuning choices, stability, validation, and domain knowledge. Complex outputs are sensitive to assumptions and should be checked with alternative specifications.