| name | expecon-identification |
| description | Use when the credibility of an Experimental Economics (ExpEcon) manuscript rests on experimental control — incentive compatibility, randomization, the no-deception gate, and clean treatment contrasts. Stress-tests design-based identification before exhibits are finalized; it does not draft prose. |
Experimental Control & Identification (expecon-identification)
When to trigger
- A referee questions whether the treatment effect is causal or an artifact of an uncontrolled difference
- Payoffs may not be incentive compatible — subjects could earn more by misreporting, or stakes are hypothetical/trivial
- Any procedure risks tripping the ESA no-deception norm (false feedback, fake co-players, rigged draws, undisclosed payoff manipulation)
- Randomization, session structure, or the order of treatments could confound the comparison
Identification at ExpEcon = control + a clean contrast
In observational economics, identification is an argument about why selection does not bias the estimate. In the lab, you manufacture identification by design: randomization plus tight control means the only systematic difference across conditions is the manipulated dimension. The job here is to verify that claim holds, on four fronts.
1. The two gates (binary; check first)
- No deception (hard gate). Experimental Economics only considers studies that do not deceive participants (检索于 2026-06；以官网为准). This is the single most common cause of an ExpEcon desk reject. Deception includes: false information about other participants or their choices, fabricated feedback, rigged "random" draws, misrepresenting payoffs or the true purpose in a payoff-relevant way, and confederates posing as subjects. Acceptable practices that are not deception: withholding (not misstating) information, abstract/neutral framing, the strategy method, and not naming the hypothesis. If a design needs deception to work, it cannot be saved by disclosure — redesign it.
- Salient real incentives. Choices must be incentivized with real consequences. Verify the payment mechanism is incentive compatible: BDM/random-lottery for valuations and risk, strategy-method payoffs that match the decision being elicited, one-randomly-paid-round to avoid wealth/hedging effects, and truthful-reporting mechanisms where beliefs are elicited (e.g., a proper scoring rule, ideally binarized/BSR to be robust to risk preferences). State the ECU→money conversion and the realized average payment.
2. Randomization & control
- Random assignment to treatment, and document the unit (individual, group, session). Report balance on observables and on a comprehension measure.
- Hold everything else fixed: identical instructions except the manipulated clause, same interface, same matching protocol, same subject pool and recruitment (e.g., ORSEE/hroot), same physical/online conditions.
- Beware session-level confounds: if a treatment was run in different sessions/cohorts than the control, session is confounded with treatment — randomize within session or run treatments interleaved.
3. Comprehension, order, and learning
- Comprehension checks before play; report pass rates and pre-specify how failures are handled (exclude vs. retain). A treatment difference driven by differential comprehension is not the mechanism.
- Order/learning effects: if within-subject, counterbalance order and test for order effects; if between-subject, justify the loss of power against the gain in clean identification.
4. The estimand and inference unit
- State the estimand in one sentence: the average treatment effect of [manipulation] on [primary outcome], for [population]. Distinguish it from any structural parameter.
- The independent unit is usually the session or matching group, not the individual decision (decisions within a group are not independent). Inference must respect this — see
expecon-robustness.
Execution bridge (StatsPAI / Stata MCP)
Estimate and audit the design, don't only describe it. Full map:
execution-with-mcp. Experimental Economics is lab/field experiments; randomization inference, romano_wolf for many treatments/outcomes, and power are decisive — observational tools secondary.
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
Anti-patterns
- Any deception, however mild, presented as harmless — this is the classic ExpEcon desk reject
- Hypothetical or token stakes treated as "incentivized"
- A non-incentive-compatible belief elicitation (e.g., flat-payment guesses) read as truthful beliefs
- Treatment run in separate sessions from control, so session and treatment are confounded
- Reporting per-decision n as if decisions were independent observations
- Calling a comprehension-driven gap "the behavioral effect"
Worked vignette (illustrative)
A trust-game variant gives second movers feedback on first movers' transfers. To boost a treatment, the authors consider inflating the displayed transfer. That is deception — desk-reject territory. The fix preserves identification without lying: run a strategy-method condition where second movers respond to every possible transfer, so the contrast is built from truthful, fully-incentivized responses and no feedback needs to be faked. Power is then justified at the matching-group level (e.g., 18 groups/arm for 80% power on a 1-token gap, illustrative).
Referee pushback mapped to the fix
- "Is this deception?" → Name the procedure, classify it against the ESA definition (withholding/abstract framing/strategy method = OK; false feedback/fake co-players/rigged draws = not OK), and quote the instructions.
- "The belief elicitation isn't incentivized." → Switch to a proper/binarized scoring rule and report it; flat-payment beliefs are not data.
- "Treatment is confounded with session." → Show treatments were interleaved or randomized within session, or re-run; do not hand-wave.
- "The effect is just confusion." → Report comprehension pass rates by treatment and re-run excluding failers; show the gap persists.
- "What is the estimand?" → State the ATE in one sentence and the population it speaks to; separate it from any structural parameter.
Quick incentive-compatibility reference
| Object elicited | Incentive-compatible mechanism |
|---|
| Valuation / WTP | BDM, or second-price/random-price |
| Risk preference | one-randomly-paid lottery menu (e.g., Holt–Laury), paid for real |
| Beliefs | proper scoring rule, ideally binarized (BSR) to be risk-robust |
| Strategy across states | strategy method, with payoff for the realized contingency |
| Repeated-game earnings | one-randomly-selected-round payment to avoid wealth/hedging |
If an elicited object is not on an incentive-compatible footing, the data for that object are suggestive at best — fix it before claiming it identifies anything.
Output format
【Journal】Experimental Economics (ESA method flagship)
【Skill】expecon-identification
【Verdict】pass / revise / reroute
【No-deception gate】clear / borderline-defended / FAILS
【Incentive compatibility】mechanism per elicited object + ECU→money + realized pay
【Randomization & control】unit, balance, session-confound check
【Comprehension / order】pass rates + handling; counterbalancing
【Estimand】ATE of [X] on [Y] for [pop]; inference unit = session/group
【Next skill】expecon-robustness
