Propensity weighting
Set up, run, and review this analysis in Lifescio.
Supported analysis
Choose ATE for the overall population, ATT for treated participants, or overlap for people with treatment equipoise. The app fits logistic propensity, computes the corresponding weights and refits models within a percentile bootstrap.
Supported scope: Identification requires no unmeasured confounding, positivity, consistency and correct time alignment. The app does not automatically trim propensities. Extreme overlap problems or many failed bootstrap fits call for revisiting the design, not merely increasing replications.
Prepare data and configure the analysis
Enter treatment 0/1, outcome, and baseline numeric confounders. Choose ATE, ATT, or overlap according to the population in your question, not according to which result is significant. The application fits an unpenalized logistic propensity model and uses normalized weighted outcome means within each arm.
Bootstrap replications controls repeated person-level resampling, with the propensity model refitted each time. Use a reproducibility seed to reproduce a run. No propensity trimming is performed automatically, and the tool does not fit longitudinal treatment or censoring weights.
Input reference (3 fields)
Enter your data and prespecified choices. Ranges include both limits. For table fields, use cell editing, Paste data, or Import CSV / TSV. Review whether the first imported row contains headings and map the source columns before applying the import. Empty numeric cells are not treated as zero. Open file is for an exported Lifescio analysis JSON file, not a spreadsheet.
| Input | Format | Allowed range | Guidance |
|---|---|---|---|
| Treatment, outcome and confounders | Table | — | Treatment is 0/1. Continuous or binary outcome. Remaining columns are baseline numeric confounders. Columns in order: Treatment; Outcome; Age; Covariate. |
| Estimand | Selection | — | Choices: ATE, ATT, overlap. |
| Bootstrap replications | Whole number | 50–1000 | — |
Run and review
- Give the analysis a descriptive name and review every example value. Record units, data preparation, and planning assumptions in Reproducibility & notes. For simulations, record the seed and replication or sampling settings.
- Choose Run analysis. Resolve input errors before interpreting output. In the connected app, a run uses a saved input revision; local preview runs need the page to stay open.
- Review Diagnostics for method limitations, warnings, and available numerical checks. A completed run means computation finished; it does not establish that the assumptions fit your study.
- Use the Result table selector to inspect each output. Editing inputs does not recalculate existing results: an earlier-input notice means you need to run again to obtain matching results.
Understand the outputs
Treatment effect reports the chosen estimand, weighted mean difference, bootstrap standard error, and percentile 95% interval. Read Covariate balance and Overlap and weights alongside it, especially effective N and maximum weight in each group.
The balance chart uses absolute standardized mean differences, whereas the table retains their signs. Diagnostics state how many bootstrap fits succeeded. The interval reflects the refitted procedure; it is not proof that causal assumptions hold.
Troubleshooting and common mistakes
Near-perfect separation, nonvarying confounders, or too many failed bootstrap fits stop estimation. Check coding and data support before changing model inputs. The application requires at least 80% of bootstrap fits to succeed.
If effective N is much smaller than observed N, inspect the extreme-score warning and whether the intended target is supported. Switching to overlap weights changes the population and should be documented. More bootstrap draws do not resolve absent overlap.
After correcting inputs, run again and check that the results belong to the new input snapshot. Increasing simulation draws improves numerical precision only; it does not repair invalid data or an unsuitable model.
Save, compare, and export
In the connected app, Save analysis retains the inputs in your account. Run history lets you inspect earlier runs, each with the input revision it used. In local preview, Save analysis downloads an input file instead; download results before leaving the page.
Use Result + inputs for a JSON archive containing the completed result and its inputs, Download CSV for the selected result table, PDF report for a report, and a chart's SVG button for its figure. Table displays round to three decimals; CSV and JSON exports retain calculation precision. Use those values when assessing a result near a decision boundary.
To reopen an archive, select the matching analysis method and choose Open file. Imported inputs start a new draft; a result archive can also display its retained result. It does not attach itself to an existing account analysis or recompute the result. Save or run the imported draft when you want a new account record.