Start by defining propensity score matching (PSM) as a causal inference technique for observational data, then clearly state its core assumptions (conditional ignorability, overlap, SUTVA). Walk through a step-by-step implementation: estimate propensity scores, match treated and control units, assess covariate balance, and estimate treatment effects. Emphasize that balance checking is critical and should be done before interpreting results.
Pro tip: At Amazon, where experimentation is central, highlight that PSM is a fallback when randomization isn't possible, but always acknowledge its limitations and suggest sensitivity analysis to test robustness against unmeasured confounding.
Explain that PSM is a statistical matching technique used to estimate causal effects in observational studies by balancing observed covariates between treated and control groups. It mimics randomization by creating a comparable control group based on propensity scores.
List and briefly explain the assumptions: conditional ignorability (no unmeasured confounding), overlap (common support), and stable unit treatment value assumption (SUTVA). Mention that violation of these can bias results.
Describe the process: (1) select covariates, (2) estimate propensity scores (e.g., logistic regression), (3) choose a matching algorithm (nearest neighbor, caliper, etc.), (4) perform matching, (5) check balance, (6) estimate treatment effect on matched sample.
Explain methods to assess balance: standardized mean differences (SMD) < 0.1, variance ratios, visual inspection (e.g., love plots), and statistical tests (e.g., t-tests). Emphasize that balance should be checked on all covariates used in the model.
After achieving balance, estimate the average treatment effect on the treated (ATT) using matched samples. Conduct sensitivity analysis to assess robustness to unmeasured confounding.
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