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IV and LATE — Examples
1 · Prerequisites
- Binary Operations, Monoids, Groups and Subgroups
- Construction of the Natural Numbers
- Construction of the Real Numbers via Cauchy Sequences
- Construction of the Real Numbers via Dedekind Cuts
- Cosets, Index and Lagrange's Theorem
- Countability and Uncountability
- Divisibility, Euclidean Domains, Principal Ideal Domains and Unique Factorisation
- Finite Counting, Factorials and Binomial Coefficients
- Foundations of the Real Numbers for Analysis
- Ideals, Quotient Rings and the Isomorphism Theorems for Rings
- Independence Borel Cantelli and Zero One Laws
- IV and LATE
- limsup, liminf, and Subsequential Limits
- Measurable Functions and Simple Approximation
- Measures and Their Basic Properties
- Monotone Sequences, Bolzano-Weierstrass, and Cauchy Completeness
- Normal Subgroups and Quotient Groups
- Order, Zorn's Lemma, and the Axiom of Choice
- Pointwise Extended-Real Function Products
- Polynomial Rings, the Division Algorithm and Roots
- Probability Spaces Random Variables and Expectation
- Product Measures and the Fubini Tonelli Theorems
- Relations, Functions, and Quotients
- Rings, Subrings, Integral Domains and Fields
- Roots, Rational Powers, and Classical Inequalities
- Sequences and Limits
- Sigma Algebras and Borel Sets
- Simple Field Extensions and the Construction of the Complex Numbers
- Statistical Experiments, Models and Identifiability
- Suprema and Infima
- The Lebesgue Integral and the Convergence Theorems
- The ZFC Axioms and the Basic Set Constructions
- Topological Spaces and Continuity
- Topology of ℝ
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
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.
Consistency, exclusion, joint instrument independence and relevance are the IV conditions defined in the setup. (Binary instrumental-variable potential-outcome setup)
The population ATE is . (Binary-instrument principal strata and LATE)
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)
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 always-takers, never-takers, compliers and defiers. Independently assign fair binary by taking the product of this type law with a fair Bernoulli law. In both models use treatment pairs for those four types, and let .
Specify potential outcome pairs as follows:
- Model : always , never , complier , defier .
- Model : always , never , complier , defier .
In both models set and . No monotonicity or constant treatment effect is imposed.
In the product law, is independent of all type-specific potential values, and both assignment atoms have positive probability. The observed treatment and outcome satisfy consistency by construction; is exclusion. The first-stage contrast is , so relevance holds in both models.
For , both models give the same observed pairs: always-takers have , never-takers and compliers have , and defiers have . For , both give to always-takers, to never-takers and defiers, and to compliers. Since type probabilities and the law of also agree, the entire observed laws of agree.
The reduced-form contrast in either model is . Thus the common Wald ratio is by [F4]. However, , whereas . 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
A small first stage magnifies reduced-form changes
Example
Consider two hypothetical pairs of observed mean contrasts. In each, the first-stage contrast is ; the reduced-form contrasts are and . The ratio formula gives and , 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: and, in turn, or . Compute the two Wald ratios and their difference.
The Wald ratio is when . (Binary-instrument Wald ratio)
Verification
Since , the two ratios are defined by [F1] and equal and .
Their difference is , exactly the reduced-form change divided by the common first stage . The calculation is a property of the ratio only and implies no sampling or causal conclusion.
A direct encouragement effect changes the Wald ratio
Example
Let the target population have equally likely types and let be an independent fair binary assignment. Set for type and for type . Define , and observe and . The exclusion restriction fails because changing changes even at fixed ; the treatment effect at either fixed is .
Facts & Assumptions
Given: The two-type population and structural equations in the Example. Compute the first stage, reduced form and Wald ratio.
The setup defines consistency, exclusion and the binary event-conditioned means. (Binary instrumental-variable potential-outcome setup)
The Wald ratio is the reduced-form contrast divided by the first-stage contrast when the latter is nonzero. (Binary-instrument Wald ratio)
Verification
The product assignment makes independent of the type and hence of all potential values; consistency holds by construction. Under , half the population has and the mean outcome is ; under , for both types and the outcome mean is . Thus and .
By [F2], the Wald ratio is , although the treatment contrast equals for every unit and each fixed . The discrepancy comes from the violated exclusion restriction, not treatment-effect heterogeneity.
Defiers can make the Wald ratio differ from the complier effect
Example
Take a population with type probabilities always-takers, never-takers, compliers, and defiers. Independently randomize with probabilities . Let for all types and let for always-takers, never-takers, compliers and defiers, respectively. Set 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.
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)
The observed outcome contrast equals the expectation of the potential outcome difference weighted by under the core IV assumptions. (IV observed contrasts as potential-outcome contrasts)
The Wald ratio divides the outcome contrast by the first-stage contrast. (Binary-instrument Wald ratio)
Verification
The first-stage contrast is . By [F2], the outcome contrast is ; always-takers and never-takers contribute zero.
The Wald ratio is by [F3]. The complier effect is , whereas the defier contribution enters the numerator with a negative sign. Thus the LATE conclusion cannot be used without monotonicity.
A finite randomized encouragement example
Example
Take four equally likely unit types: one always-taker with and ; one never-taker with and ; and two compliers with and outcome pairs and . Independently assign with probabilities , impose consistency and exclusion, and let and . 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.
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)
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
On the product of the four equally likely types and the fair assignment, is independent of the potential values. The two compliers have effects and , while the always-taker and never-taker both have treatment effect ; thus , , and .
The treatment rate is under and under , so . The outcome mean is under and under , so . Therefore , agreeing with the LATE in [F2] and differing from the ATE.
With heterogeneous effects, LATE can differ from ATE
Example
Take four equally likely types: an always-taker with treatment effect , a never-taker with effect , and two compliers with effects and . Let all , let equal those effects, and let 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.
ATE and the complier event-conditioned mean are defined from the potential outcomes and principal strata. (Binary-instrument principal strata and LATE)
Under the stated IV assumptions, the Wald ratio equals the complier average effect. (The Wald ratio identifies the complier average treatment effect)
Verification
The complier event has probability and its two effects are and , so . Averaging all four effects gives .
The always-taker and never-taker outcomes do not change across instrument arms. The two compliers each have probability , so their treatment effects give reduced-form contrast ; the first-stage contrast is . The Wald ratio is therefore , equal to the LATE by [F2] but not to the population ATE.
Sources
- Hernán and Robins, Causal Inference: What If, Chapter 16, Technical Points 16.2 and 16.7, printed pp. 212 and 224–225
- Hyunseung Kang, Causal Inference: Identification Under Unmeasured Confounding (Instrumental Variables), LATE and ATE discussion
- Judea Pearl, Causal Inference in Statistics: An Overview, §3.5.2–§3.5.4, journal pp. 122–126
- Hernán and Robins, Causal Inference: What If, Chapter 16, §§16.2 and 16.5, printed pp. 212–222
- Hyunseung Kang, Causal Inference: Identification Under Unmeasured Confounding (Instrumental Variables), instrument strength and LATE sections
- Hernán and Robins, Causal Inference: What If, Chapter 16, Technical Point 16.1 and §16.5, printed pp. 210–222
- Hyunseung Kang, Causal Inference: Identification Under Unmeasured Confounding (Instrumental Variables), exclusion restriction section
- Judea Pearl, Causal Inference in Statistics: An Overview, §3.5.2 and §3.5.4, journal pp. 122–126
- Hernán and Robins, Causal Inference: What If, Chapter 16, §16.4 and Technical Point 16.7, printed pp. 217–225
- Hyunseung Kang, Causal Inference: Identification Under Unmeasured Confounding (Instrumental Variables), Compliance Types and LATE proof
- Hyunseung Kang, Causal Inference: Identification Under Unmeasured Confounding (Instrumental Variables), randomized encouragement example and LATE proof
- Hernán and Robins, Causal Inference: What If, Chapter 16, §§16.3–16.4 and Technical Point 16.7, printed pp. 214–225