Alphabeta Math
Pipeline-generated
How statement and proof provenance work

The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.

  • Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
  • AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.

These labels describe origin, not correctness: citations and verification chips remain separate evidence.

✓ 6 results · all verified · 1 also independently AI-judged
Every result on this page is machine-checked by a proof checker and read in full and owner-audited; the judge is an additional, independent cross-model AI review of the proofs. The 5 not AI-judged were verified by owner audit (typically over a confirmed judge false positive), not failures.

IV and LATE — Examples

1 · Prerequisites

2 · Summary

These worked cases separate the ratio's arithmetic from the conditions that give it a causal interpretation. A small first stage can magnify changes in the reduced form; a direct instrument effect breaks exclusion; defiers can make the ratio differ from the complier effect; and heterogeneous effects can make a valid LATE differ from the ATE. The final counterexample gives two full-data laws with the same observed law and different population ATEs when monotonicity and effect homogeneity are not assumed. None of these examples turns model assumptions into empirical facts.

3 · Logical flowchart

4 · Definitions, theorems and proofs

CounterexampleConstruction: AI-generatedVerification: AI-generatedprecheck passOpen item page →

Core IV conditions alone do not identify the population ATE

Statement refuted

The binary-IV core conditions—consistency, joint instrument independence, exclusion and relevance—by themselves identify the population ATE, without monotonicity or a homogeneity restriction.

Facts & Assumptions

Given: Two full-data laws with the same induced observed law, both satisfying the core IV conditions, but with different ATEs. Verify that this is a counterexample to identifiability.

[F1]

Consistency, exclusion, joint instrument independence and relevance are the IV conditions defined in the setup. (Binary instrumental-variable potential-outcome setup)

[F2]

The population ATE is E[Y(1)−Y(0)]. (Binary-instrument principal strata and LATE)

[F3]

An estimand is a target function on the model, and identifiability means equal observed laws force equal target values. (Estimands on a statistical model, Identifiability of an estimand)

[F4]

The Wald ratio is the observed outcome contrast divided by the first-stage contrast. (Binary-instrument Wald ratio)

Counterexample

Let the unit type probabilities be 0.2 always-takers, 0.3 never-takers, 0.4 compliers and 0.1 defiers. Independently assign fair binary Z by taking the product of this type law with a fair Bernoulli law. In both models use treatment pairs (A(0),A(1))=(1,1),(0,0),(0,1),(1,0) for those four types, and let Y(a,z)=Y(a).

Specify potential outcome pairs (Y(0),Y(1)) as follows:

  • Model M0: always (0,5), never (0,0), complier (0,1), defier (0,3).
  • Model M1: always (10,5), never (0,10), complier (0,1), defier (0,3).

In both models set A=A(Z) and Y=Y(A,Z). No monotonicity or constant treatment effect is imposed.

1.1givenF1constructalgebra

In the product law, Z is independent of all type-specific potential values, and both assignment atoms have positive probability. The observed treatment and outcome satisfy consistency by construction; Y(a,z)=Y(a) is exclusion. The first-stage contrast is E[A∣Z=1]−E[A∣Z=0]=(0.2+0.4)−(0.2+0.1)=0.3≠0, so relevance holds in both models.

2.1givenstep 1.1construct

For Z=0, both models give the same observed pairs: always-takers have (A,Y)=(1,5), never-takers and compliers have (0,0), and defiers have (1,3). For Z=1, both give (1,5) to always-takers, (0,0) to never-takers and defiers, and (1,1) to compliers. Since type probabilities and the law of Z also agree, the entire observed laws of (Z,A,Y) agree.

3.1F2F3F4step 2.1algebra∎

The reduced-form contrast in either model is ΔY=(0.2⋅5+0.4⋅1)−(0.2⋅5+0.1⋅3)=0.1. Thus the common Wald ratio is 0.1/0.3=1/3 by [F4]. However, ATE⁡(M0)=0.2⋅5+0.3⋅0+0.4⋅1+0.1⋅3=1.7, whereas ATE⁡(M1)=0.2⋅(−5)+0.3⋅10+0.4⋅1+0.1⋅3=2.7. Therefore [F2] has different values at two parameters with the same observed law, and [F3] shows the ATE is not identifiable under only these core conditions.

5 · Examples, counterexamples and false statements

ExampleConstruction: AI-generatedVerification: AI-generatedprecheck passjudge pass (gpt-6-luna)Open item page →

A small first stage magnifies reduced-form changes

Example

Consider two hypothetical pairs of observed mean contrasts. In each, the first-stage contrast is ΔA=0.02; the reduced-form contrasts are ΔY=0.06 and ΔY=0.07. The ratio formula gives 3 and 3.5, respectively. This is arithmetic illustrating the ratio's sensitivity to a small denominator; it makes no claim about a sampling distribution, estimator variance, or causal validity.

Facts & Assumptions

Given: ΔA=0.02 and, in turn, ΔY=0.06 or 0.07. Compute the two Wald ratios and their difference.

[F1]

The Wald ratio is ΔY/ΔA when ΔA≠0. (Binary-instrument Wald ratio)

Verification

1.1givenF1algebra

Since 0.02≠0, the two ratios are defined by [F1] and equal 0.06/0.02=3 and 0.07/0.02=3.5.

2.1givenstep 1.1algebra∎

Their difference is 0.5, exactly the reduced-form change 0.01 divided by the common first stage 0.02. The calculation is a property of the ratio only and implies no sampling or causal conclusion.

ExampleConstruction: AI-generatedVerification: AI-generatedprecheck passOpen item page →

A direct encouragement effect changes the Wald ratio

Example

Let the target population have equally likely types c,n and let Z be an independent fair binary assignment. Set (A(0),A(1))=(0,1) for type c and (0,0) for type n. Define Y(a,z)=2a+z, and observe A=A(Z) and Y=Y(A,Z). The exclusion restriction fails because changing z changes Y even at fixed a; the treatment effect at either fixed z is 2.

Facts & Assumptions

Given: The two-type population and structural equations in the Example. Compute the first stage, reduced form and Wald ratio.

[F1]

The setup defines consistency, exclusion and the binary event-conditioned means. (Binary instrumental-variable potential-outcome setup)

[F2]

The Wald ratio is the reduced-form contrast divided by the first-stage contrast when the latter is nonzero. (Binary-instrument Wald ratio)

Verification

1.1givenF1constructalgebra

The product assignment makes Z independent of the type and hence of all potential values; consistency holds by construction. Under Z=1, half the population has A=1 and the mean outcome is (3+1)/2=2; under Z=0, A=0 for both types and the outcome mean is 0. Thus ΔA=1/2 and ΔY=2.

2.1F1F2step 1.1algebra∎

By [F2], the Wald ratio is 2/(1/2)=4, although the treatment contrast Y(1,z)−Y(0,z) equals 2 for every unit and each fixed z. The discrepancy comes from the violated exclusion restriction, not treatment-effect heterogeneity.

ExampleConstruction: AI-generatedVerification: AI-generatedprecheck passOpen item page →

Defiers can make the Wald ratio differ from the complier effect

Example

Take a population with type probabilities 0.2 always-takers, 0.3 never-takers, 0.4 compliers, and 0.1 defiers. Independently randomize Z with probabilities 1/2,1/2. Let Y(0)=0 for all types and let Y(1)=0,0,1,3 for always-takers, never-takers, compliers and defiers, respectively. Set Y(a,z)=Y(a) and observe by consistency. Exclusion and instrument independence hold, but monotonicity fails.

Facts & Assumptions

Given: The finite type distribution in the Example. Compute the two observed contrasts, the Wald ratio and the complier mean effect.

[F1]

Consistency, exclusion, joint instrument independence, event-conditioned means and the four principal strata are defined in the IV setup. (Binary instrumental-variable potential-outcome setup, Binary-instrument principal strata and LATE)

[F2]

The observed outcome contrast equals the expectation of the potential outcome difference weighted by A(1)−A(0) under the core IV assumptions. (IV observed contrasts as potential-outcome contrasts)

[F3]

The Wald ratio divides the outcome contrast by the first-stage contrast. (Binary-instrument Wald ratio)

Verification

1.1givenF1F2algebra

The first-stage contrast is P(C)−P(D)=0.4−0.1=0.3. By [F2], the outcome contrast is E[(A(1)−A(0))(Y(1)−Y(0))]=0.4⋅1−0.1⋅3=0.1; always-takers and never-takers contribute zero.

2.1F1F3step 1.1algebra∎

The Wald ratio is 0.1/0.3=1/3 by [F3]. The complier effect is 1, whereas the defier contribution enters the numerator with a negative sign. Thus the LATE conclusion cannot be used without monotonicity.

ExampleConstruction: AI-generatedVerification: AI-generatedprecheck passOpen item page →

A finite randomized encouragement example

Example

Take four equally likely unit types: one always-taker with (A(0),A(1))=(1,1) and (Y(0),Y(1))=(0,4); one never-taker with (0,0) and (0,4); and two compliers with (0,1) and outcome pairs (0,1) and (0,3). Independently assign Z with probabilities 1/2,1/2, impose consistency and exclusion, and let A=A(Z) and Y=Y(A). The model has no defiers, but the effects vary by type.

Facts & Assumptions

Given: The finite law described in the Example. Verify its first stage, reduced form, LATE, ATE and Wald ratio.

[F1]

The binary IV setup defines consistency, exclusion, independence, relevance, the event-conditioned means, and the four principal strata. (Binary instrumental-variable potential-outcome setup, Binary-instrument principal strata and LATE)

[F2]

Under the stated relevance, exclusion, independence, consistency and monotonicity assumptions, the Wald ratio equals the complier mean effect. (The Wald ratio identifies the complier average treatment effect)

Verification

1.1givenF1constructalgebra

On the product of the four equally likely types and the fair assignment, Z is independent of the potential values. The two compliers have effects 1 and 3, while the always-taker and never-taker both have treatment effect 4; thus P(C)=1/2, LATE⁡=(1+3)/2=2, and ATE⁡=(4+4+1+3)/4=3.

2.1F1F2step 1.1algebra∎

The treatment rate is 3/4 under Z=1 and 1/4 under Z=0, so ΔA=1/2. The outcome mean is 2 under Z=1 and 1 under Z=0, so ΔY=1. Therefore ψW=1/(1/2)=2, agreeing with the LATE in [F2] and differing from the ATE.

ExampleConstruction: AI-generatedVerification: AI-generatedprecheck passOpen item page →

With heterogeneous effects, LATE can differ from ATE

Example

Take four equally likely types: an always-taker with treatment effect 4, a never-taker with effect 4, and two compliers with effects 1 and 3. Let all Y(0)=0, let Y(1) equal those effects, and let Z be an independent fair binary assignment. The model has consistency, exclusion, instrument independence, relevance and monotonicity.

Facts & Assumptions

Given: The four equally likely types and randomized assignment described in the Example. Verify LATE, ATE and the Wald ratio.

[F1]

ATE and the complier event-conditioned mean are defined from the potential outcomes and principal strata. (Binary-instrument principal strata and LATE)

[F2]

Under the stated IV assumptions, the Wald ratio equals the complier average effect. (The Wald ratio identifies the complier average treatment effect)

Verification

1.1givenF1algebra

The complier event has probability 1/2 and its two effects are 1 and 3, so LATE⁡=2. Averaging all four effects gives ATE⁡=(4+4+1+3)/4=3.

2.1F2step 1.1algebra∎

The always-taker and never-taker outcomes do not change across instrument arms. The two compliers each have probability 1/4, so their treatment effects give reduced-form contrast 14⋅1+14⋅3=1; the first-stage contrast is 1/2. The Wald ratio is therefore 2, equal to the LATE by [F2] but not to the population ATE.

Sources