{"ate":0.1665239375097085,"ci_90":[-0.029834740813006327,0.3628826158324237],"country":"SA","covariates_used":["risk_tier","month","day_of_week","prior_shutdowns_30d","block_rate_lag7_safe","is_weekend"],"effect_modifiers":["risk_tier","gdelt_unrest_safe","country_one_hot"],"global":{"ate_holdout":0.09621258666151863,"ate_insample":0.0962125866615187,"ate_train":0.0962125866615187,"ci_coverage_holdout":0.995,"holdout_caveat":"ATE is IN-SAMPLE / associational: the causal forest is fit on ALL data and ate_holdout is identical to ate_train (no real out-of-sample holdout). Underpowered: only 5 of 21 countries have >=3 events of this treatment. Read it as an in-sample associational estimate, NOT a causally-validated out-of-sample +Xpp claim.","holdout_is_real":false,"nonzero_ci_fraction":0.6,"trained_at":"2026-05-21T12:07:33.001992Z","training_rows":12810},"honest_caveats":["only 0 election events observed for SA in training window - effect extrapolated from country one-hot + covariates, wide CI","ATE is IN-SAMPLE / associational: the causal forest is fit on ALL data and ate_holdout is identical to ate_train (no real out-of-sample holdout). Underpowered: only 5 of 21 countries have >=3 events of this treatment. Read it as an in-sample associational estimate, NOT a causally-validated out-of-sample +Xpp claim."],"method":"Causal Forest DML (Athey & Wager 2019, Chernozhukov et al. 2018)","n_country_days":610,"n_for_this_country":0,"n_holdout_events":0,"n_training_events":0,"noisy":true,"promoted":true,"schema":"voidly-sentinel-hte/v1","treatment":"election"}