You cannot randomize ad exposure. Users differ in age, education, and income. Propose a causal inference plan to estimate the ATE of ad exposure on conversions. Include: (1) a DAG to justify a valid adjustment set; (2) a propensity score model and either matching or inverse-probability weighting with stabilized weights; (3) formulas for ATE via IPW and doubly robust (AIPW) estimators; (4) diagnostics (overlap checks, standardized mean differences before/after, effective sample size, weight trimming); (5) sensitivity analysis to unobserved confounding (e.g., Rosenbaum bounds); (6) avoiding post-treatment bias (exclude engagement mediators); (7) variance estimation and uncertainty reporting. Discuss when you’d prefer diff-in-diff or CUPED and required assumptions.