data-contracts

Use the validate argument in pandas merge, such as validate="one_to_one" or "many_to_one", to enforce declared cardinality. Then assert the output row count matches the left side and tabulate NA counts in the merged-in columns to catch unmatched keys.

Pass the relationship argument to dplyr joins, for example left_join(x, y, by="id", relationship="many-to-one"), which errors on violations. Combine with anti_join to inspect unmatched keys and stopifnot to bracket row counts around the merge.

Yes, Stata ships contract primitives natively: isid checks key uniqueness, merge with assert() enforces match cardinality, and datasignature freezes and verifies dataset state. The reference helpers include a full Stata prelude alongside Python, R, and Julia.

Silent failures like join fan-outs, NA-poisoned aggregations, unit mismatches, and vintage-mismatched keys run cleanly while corrupting results. Asserting invariants such as row counts, totals reconciliation, and missingness maps at each pipeline boundary exposes these failures before they reach a reported figure.

Avoid check inventories: each check must name a specific silent failure it catches, with one check per failure mode placed at ingest, merges, sample construction, and report time. A single-join script needs roughly four checks; redundant asserts and standalone check directories add ceremony without rigor.