Propensity Score Analysis AI Prompt

Implement and evaluate a propensity score analysis to estimate a causal effect from observational data.

Treatment variable: {{treatment}} (binary: treated vs untreated)
Outcome: {{outcome}}
Potential confounders: {{confounders}}
Data: {{data_description}}

1. Estimand clarification:
   - ATE (Average Treatment Effect): the effect if the entire population were treated vs untreated
   - ATT (Average Treatment Effect on the Treated): the effect for those who actually received treatment
   - ATC: average effect for the controls if they had been treated
   - Choose based on the scientific question; ATT is most common in observational studies

2. Propensity score estimation:
   PS = P(Treatment = 1 | X)
   - Fit a logistic regression with treatment as the outcome and all confounders as predictors
   - Include: all variables that affect the outcome OR that affect both treatment and outcome
   - Do NOT include: instrumental variables (variables affecting treatment but NOT outcome)
   - Do NOT include: colliders (effects of treatment or outcome)
   - Check common support: there should be overlap in PS distributions between treated and controls
     Limited overlap = inability to estimate causal effect for some subgroups

3. PS matching:
   - Nearest neighbor matching: each treated unit matched to the closest control by PS
   - Caliper matching: require |PS_treated - PS_control| < 0.2 x SD(PS) (discard poor matches)
   - 1:1 vs 1:k matching: k>1 increases precision but introduces bias if poor matches are forced
   - Matching with replacement: allows controls to be reused (reduces bias, increases variance)

4. Balance assessment:
   - Standardized mean differences (SMD) before and after matching for each confounder
   - SMD < 0.10 after matching: good balance
   - Love plot: visualize SMD for each confounder before and after adjustment
   - Do NOT use p-values for balance checking — they are affected by sample size, not balance

5. Analysis after matching:
   - Estimate the treatment effect using a paired or stratified outcome model
   - Use doubly robust estimation: combine PS weighting with outcome regression (consistent if either is correct)
   - Report: estimated causal effect, 95% CI (using robust standard errors), and the matched sample size

6. Sensitivity analysis for unmeasured confounding:
   - Rosenbaum bounds: how strong would an unmeasured confounder need to be to explain away the result?
   - Gamma parameter: if Gamma = 2, the odds of treatment could differ by a factor of 2 for matched pairs with identical observed covariates
   - E-value: minimum association a confounder would need with both treatment and outcome to fully explain the observed effect

Return: PS model specification, overlap assessment, balance table (pre/post), causal effect estimate, and sensitivity analysis.