Inference Configuration

The shipped inference YAML is the canonical example. Active keys are listed below; unknown non-sparse_sc_* keys are not comprehensively rejected, so a misspelling can be ignored. Compare production configs with this table.

Key Type/default Contract
schema_version string, 1.0 Supported config schema.
data_path string Input CSV for file mode.
location_col_name string Geography identifier column.
date_col_name string Period column.
date_format string Explicit pandas date format.
outcome_col_name string Numeric outcome column.
covariate_col_names list or null Optional covariates passed to the loader.
treatment_unit_ids list Treated geography identifiers.
intervention_date_str string First observed treated period.
cooldown_periods non-negative integer, 0 Observed periods excluded after launch.
measurement_start_date_str string or null Optional explicit start; must agree with cooldown.
duplicate_policy error, mean, sum Duplicate unit-period handling.
missing_outcome_policy error, drop_unit, drop_period, impute_with_report Incomplete-panel handling.
min_pre_treatment_periods integer Minimum pre-period observations when supplied.
min_post_treatment_periods integer Minimum measured post-period observations when supplied.
estimator sparsesc Only implemented estimator selector.
output_dir string Artefact directory.
create_plots boolean CLI plot default.

SparseSC runtime keys:

Key Default or role
sparse_sc_model_type SparseSC model type, normally retrospective.
sparse_sc_fast_estimation Selects SparseSC fast fitting path.
sparse_sc_return_ci Requests placebo confidence intervals.
sparse_sc_T0, sparse_sc_T1 SparseSC history-length controls; not cooldown semantics.
sparse_sc_max_n_pl Maximum placebo assignments used.
sparse_sc_placebo_seed Non-negative seed for sampled placebo assignments; default 110011.
sparse_sc_level Requested interval level.
sparse_sc_lasso_max_iter, sparse_sc_lasso_tol Forwarded as fast-path CV options; the RidgeCV-backed path may ignore them. They do not configure the full path.
sparse_sc_cv_folds, sparse_sc_scoring, sparse_sc_gcv_mode Supported adapter controls.

Assumption keys are run_assumption_checks, require_assumption_checks, fail_on_assumption_error, parallel_trends_method, spillover_method, and assumption_alpha. Only the parallel-trends diagnostic is gate-eligible. The interference screen is always advisory.

The non-prefixed compatibility keys lasso_selection, lasso_fit_intercept, and lasso_normalize are forwarded only when the fast path is selected. Their support depends on the installed scikit-learn API; prefer the documented sparse_sc_* controls for new configurations.

There is no active treatment_col or end_date key. Define treatment through treatment_unit_ids; trim the input panel to a pre-specified end date. Inference also does not consume shapemap_file or shapemap_id_column; shapemap-backed maps belong to the donor stage.