Jhe Identification

Identification Strategy (jhe-identification)

When to trigger

• A causal health-policy claim rests on OLS + controls or TWFE on staggered state adoptions
• An insurance/treatment effect is contaminated by selection that is not modeled
• An eligibility cutoff (income, age-65 Medicare, kink in subsidy schedule) is used without an RD defense
• A structural insurance-demand or provider-response parameter is estimated but it is unclear what identifies it
• You are unsure the design clears JHE's credible-causal-plus-institutional bar

The JHE identification bar

JHE referees demand credible causal identification and institutional realism: the mapping from a source of variation to the health-economics estimand must be explicit, falsifiable, and consistent with how the program or market actually works. Two failure modes get punished hardest here: (1) ignoring selection — into insurance, into treatment, into the sample — that the health setting makes first-order; and (2) treating a policy variation as exogenous when institutional detail (phase-ins, waivers, simultaneous reforms, anticipation) says it is not. State the estimand, name the assumption, show the diagnostic that could have failed, and keep the claim inside what the design supports. Inference clusters at the policy/assignment level (usually state).

Design paths

Path A: Quasi-experimental health-policy variation (DiD / event study)

• Medicaid/Medicare expansions, insurance mandates, state reforms: with staggered timing move beyond TWFE (Callaway–Sant'Anna, Sun–Abraham, de Chaisemartin–D'Haultfœuille, Borusyak–Jaravel–Spiess).
• Clean event-study with leads for pre-trends; Goodman-Bacon decomposition; Rambachan–Roth honest-DID sensitivity.
• Rule out concurrent reforms (other ACA provisions, simultaneous payment changes) and anticipation/woodwork effects — the institutional threat referees raise first.

Path B: Selection into insurance / treatment

• Make the selection model explicit: is identification from random/quasi-random assignment, or from a selection-correction (Heckman, control function, bounds)?
• For coverage effects, separate take-up, crowd-out, and ex-post moral hazard; a reduced-form "coverage effect" that blends them is not identified for welfare.
• Where selection is unavoidable, report bounds (Lee/Manski) rather than asserting ignorability.

Path C: Eligibility RD / kink

• Age-65 Medicare, income-threshold subsidies, BMI/clinical cutoffs: density test (McCrary / Cattaneo–Jansson–Ma) for manipulation; covariate smoothness; local-linear with data-driven bandwidth and bias-corrected robust CIs (rdrobust).
• State the estimand is the local effect at the cutoff and resist extrapolation to the whole eligible population.

Path D: IV / structural identification

• IV (e.g., distance-to-provider, simulated eligibility, judge/examiner leniency): strong first stage (effective F), exclusion argued from institutions + falsification; weak-IV-robust sets (Anderson–Rubin).
• Structural demand/provider models: name what identifies each parameter (a price/cost-sharing kink identifies the demand elasticity; a coverage discontinuity identifies the selection parameter), report sensitivity to identifying moments, and Monte Carlo recovery.

Execution bridge (StatsPAI / Stata MCP)

Estimate and audit the design, don't only describe it. Full map: execution-with-mcp. JHE is health economics — insurance/program reforms and selection; foreground DiD/IV/RDD and selection corrections.

• detect_design → recommend → fit with as_handle=true → audit_result.
• Observational causal claims: staggered DiD (callaway_santanna / sun_abraham + bacon_decomposition + honest_did_from_result); IV (effective_f_test + anderson_rubin_ci); RDD (rdrobust + mccrary_test).
• Experiments: randomization-based inference + romano_wolf for many-outcome control.
• Sensitivity: oster_delta / sensemakr for observational claims.

Report the magnitude in interpretable units; route the full battery to the appendix. A run end-to-end (synthetic data, real returns) is in the JF execution walkthrough.

Checklist

• Design chosen; the variation-to-estimand mapping stated in one sentence
• Estimand named (ITT / LATE / ATT / local-at-cutoff / structural parameter) and matched to the design
• Selection threat addressed explicitly (modeled, bounded, or argued away with evidence)
• Institutional confounds ruled out: concurrent reforms, phase-ins, anticipation, woodwork
• Design-appropriate diagnostic shown (pre-trends+Bacon / density+bandwidth / first-stage+exclusion / moment sensitivity)
• Modern estimator where TWFE or 2SLS would bias; inference clustered at the policy level
• The claim never exceeds the design (coverage effect ≠ health-production effect ≠ welfare)

Anti-patterns

• TWFE on staggered Medicaid/state adoptions with no heterogeneity-bias discussion
• A "coverage effect" read as a health or welfare effect with no take-up/crowd-out/moral-hazard decomposition
• Treating a policy as exogenous while ignoring simultaneous reforms or anticipation
• RD on an eligibility cutoff with no density/manipulation test or a hand-picked bandwidth
• Asserting an instrument is exogenous (distance, simulated eligibility) with no falsification
• Clustering below the policy level so standard errors are understated