Experimentation.ExposureMappingInterference.Variance
Design-based variance estimators for exposure-mapping HT means and effects, including conservative estimators under pairwise exposure conditions.
Conservative 14 core · 0 supporting Design-based variance estimators for Var[ŷᵀ(d)] and Var[τ̂(dk,dl)]. ★ E_htVarEst★ E_htCovEst_le★ E_htEffectVarEst_ge★ E_htVarEst_eq_addBias★ E_htVarEst_add_htA2_ge★ E_htCovEst_eq_of_noEffect★ E_htCovEstA_le★ E_htEffectVarEstA_ge
Conservative variance estimators (Aronow–Samii 2017, §5)
Design-based variance estimators for Var[ŷᵀ(d)] and Var[τ̂(dk,dl)]. In the
positive-joint regime (π_{ij}(d) > 0, π_{ij}(d,d') > 0 for i ≠ j) the variance
estimator htVarEst is exactly unbiased (Lemma 5.1, varun). The covariance
Cov[ŷᵀ(dk),ŷᵀ(dl)] is unidentified, so its estimator htCovEst is only nonpositively
biased (Prop 5.4, ncov, via Young's inequality 2ab ≤ a²+b² for the diagonal term).
Assembling these gives the conservative effect-variance estimator htEffectVarEst with
nonnegative bias (Prop 5.7, consvar) — the input the interval result consumes.
The zero-pairwise refinements handle the π_{ij} = 0 corner cases. Prop 5.2
(E_htVarEst_eq_addBias, varbias) characterizes the bias of htVarEst as the explicit
correction A = ∑_{π_{ij}=0} y_i y_j; the Young correction htA2 (Â₂) restores
conservativeness (Prop 5.3, E_htVarEst_add_htA2_ge, a2). Prop 5.5
(E_htCovEst_eq_of_noEffect, no_bias_cov) shows htCovEst is exactly unbiased under no
effect. The general covariance estimator htCovEstA (Ĉov_A, eq:ht_cov_general_estimator)
is nonpositively biased with no positive-cross-joint assumption (Prop 5.6,
E_htCovEstA_le, cova); its Young correction ranges over all j ∈ U with
π_{ij}(d_k,d_l)=0 (faithful to the paper — the diagonal j=i, always zero since
d_k ≠ d_l, subsumes the unidentified −∑ᵢ y_i(d_k)y_i(d_l) term). Assembling these gives
the general conservative effect-variance estimator htEffectVarEstA with nonnegative bias
(general consvar, E_htEffectVarEstA_ge).
def htVarEst reviewed
⚑ flagged
Causalean.Experimentation.ExposureMappingInterference
For a finite assignment space, a finite unit population, a unit-trait space, an exposure space, a randomization design, exposure-indexed potential outcomes, an exposure mapping, unit traits, an exposure level, and an assignment, the Horvitz--Thompson variance estimator is the sum of its single-unit inverse-propensity terms and its ordered-pair terms over distinct units.
Definition (Lean source)
parameters
Ω :
Type u_1
ι :
Type u_2
Θ :
Type u_3
Δ :
Type u_4
D :
y :
ι → Δ → ℝ
f :
Ω → Θ → Δ
θ :
ι → Θ
d :
Δ
z :
Ω
def
htVarEst D y f θ d z :
ℝ
given by
+ ∑ i, ∑ j ∈ Finset.univ.erase i, expoInd f θ i d z * expoInd f θ j d z * ((propPairSame D f θ i j d - prop D f θ i d * prop D f θ j d) / propPairSame D f θ i j d) * ((Yobs y f θ i z / prop D f θ i d) * (Yobs y f θ j z / prop D f θ j d))
def htCovEst reviewed
⚑ flagged
Causalean.Experimentation.ExposureMappingInterference
For a finite assignment space, a finite unit population, a unit-trait space, an exposure space, a randomization design, exposure-indexed potential outcomes, an exposure mapping, unit traits, two exposure levels, and an assignment, the Horvitz--Thompson covariance estimator is its ordered-pair cross-exposure term over distinct units minus its single-unit Young correction.
Definition (Lean source)
parameters
Ω :
Type u_1
ι :
Type u_2
Θ :
Type u_3
Δ :
Type u_4
D :
y :
ι → Δ → ℝ
f :
Ω → Θ → Δ
θ :
ι → Θ
dk dl :
Δ
z :
Ω
def
htCovEst D y f θ dk dl z :
ℝ
given by
(∑ i, ∑ j ∈ Finset.univ.erase i, expoInd f θ i dk z * expoInd f θ j dl z * ((propPairCross D f θ i j dk dl - prop D f θ i dk * prop D f θ j dl) / propPairCross D f θ i j dk dl) * ((Yobs y f θ i z / prop D f θ i dk) * (Yobs y f θ j z / prop D f θ j dl)))
def htEffectVarEst reviewed
⚑ flagged
Causalean.Experimentation.ExposureMappingInterference
For a finite assignment space, a finite unit population, a unit-trait space, an exposure space, a randomization design, exposure-indexed potential outcomes, an exposure mapping, unit traits, two exposure levels, and an assignment, the conservative effect-variance estimator is the sum of the two variance estimators minus twice the covariance estimator, divided by the square of the population size.
Definition (Lean source)
parameters
Ω :
Type u_1
ι :
Type u_2
Θ :
Type u_3
Δ :
Type u_4
D :
y :
ι → Δ → ℝ
f :
Ω → Θ → Δ
θ :
ι → Θ
dk dl :
Δ
z :
Ω
def
htEffectVarEst D y f θ dk dl z :
ℝ
theorem E_htVarEst reviewed
⚑ flagged
Causalean.Experimentation.ExposureMappingInterference
Lemma 5.1 (varun). In the positive-joint regime — every unit has nonzero exposure propensity under d and every off-diagonal pair has nonzero same-arm joint exposure propensity under d — the Horvitz–Thompson variance estimator is exactly unbiased for the true design variance of the HT total.
Formal statement
hypotheses
Ω :
Type u_1
ι :
Type u_2
Θ :
Type u_3
Δ :
Type u_4
D :
y :
ι → Δ → ℝ
f :
Ω → Θ → Δ
θ :
ι → Θ
d :
Δ
hpos :
∀ i, prop D f θ i d ≠ 0
hjoint :
∀ i j
if
i ≠ j
then
propPairSame D f θ i j d ≠ 0
Proof (Lean source)
theorem E_htVarEst (D : FiniteDesign Ω) (y : ι → Δ → ℝ) (f : Ω → Θ → Δ) (θ : ι → Θ)
(d : Δ) (hpos : ∀ i, prop D f θ i d ≠ 0)
(hjoint : ∀ i j, i ≠ j → propPairSame D f θ i j d ≠ 0) :
D.E (htVarEst D y f θ d) = D.Var (htTotal D y f θ d) := by
rw [Var_htTotal]
unfold htVarEst
rw [D.E_add, D.E_sum, D.E_sum]
congr 1
· -- diagonal sum
refine Finset.sum_congr rfl (fun i _ => ?_)
have key : (fun z => expoInd f θ i d z * (1 - prop D f θ i d)
* (Yobs y f θ i z / prop D f θ i d) ^ 2)
= (fun z => ((1 - prop D f θ i d) * (prop D f θ i d)⁻¹ ^ 2)
* (expoInd f θ i d z * (Yobs y f θ i z) ^ 2)) := by
funext z; rw [div_pow]; ring
rw [key, D.E_const_mul]
rw [D.E_congr (Y := fun z => expoInd f θ i d z * (y i d) ^ 2)
(fun z => expoInd_mul_Yobs_sq y f θ i d z)]
rw [D.E_mul_const, E_expoInd]
ring
· -- off-diagonal sum
refine Finset.sum_congr rfl (fun i _ => ?_)
rw [D.E_sum]
refine Finset.sum_congr rfl (fun j hj => ?_)
have hji : j ≠ i := (Finset.mem_erase.mp hj).1
have hij : i ≠ j := fun h => hji h.symm
have hjoint_ij := hjoint i j hij
have hi := hpos i
have hjp := hpos j
-- factor constants out of the expectation
have key : (fun z => expoInd f θ i d z * expoInd f θ j d z
* ((propPairSame D f θ i j d - prop D f θ i d * prop D f θ j d)
/ propPairSame D f θ i j d)
* ((Yobs y f θ i z / prop D f θ i d) * (Yobs y f θ j z / prop D f θ j d)))
= (fun z => (((propPairSame D f θ i j d - prop D f θ i d * prop D f θ j d)
/ propPairSame D f θ i j d) * ((prop D f θ i d)⁻¹ * (prop D f θ j d)⁻¹))
* (expoInd f θ i d z * expoInd f θ j d z
* Yobs y f θ i z * Yobs y f θ j z)) := by
funext z; ring
rw [key, D.E_const_mul]
-- now evaluate E of the product of indicators times both observed outcomes
have hprod : D.E (fun z => expoInd f θ i d z * expoInd f θ j d z
* Yobs y f θ i z * Yobs y f θ j z)
= y i d * y j d * propPairSame D f θ i j d := by
rw [D.E_congr (Y := fun z => (y i d * y j d)
* (expoInd f θ i d z * expoInd f θ j d z)) (fun z => ?_)]
· rw [D.E_const_mul]; rfl
· -- force both outcomes via expoInd₂_mul_Yobs and its symmetric use
have h1 : expoInd f θ i d z * expoInd f θ j d z * Yobs y f θ i z
= expoInd f θ i d z * expoInd f θ j d z * y i d :=
expoInd₂_mul_Yobs y f θ i d (expoInd f θ j d) z
have h2 : expoInd f θ j d z * expoInd f θ i d z * Yobs y f θ j z
= expoInd f θ j d z * expoInd f θ i d z * y j d :=
expoInd₂_mul_Yobs y f θ j d (expoInd f θ i d) z
calc expoInd f θ i d z * expoInd f θ j d z * Yobs y f θ i z * Yobs y f θ j z
= (expoInd f θ i d z * expoInd f θ j d z * Yobs y f θ i z) * Yobs y f θ j z := by ring
_ = (expoInd f θ i d z * expoInd f θ j d z * y i d) * Yobs y f θ j z := by rw [h1]
_ = y i d * (expoInd f θ j d z * expoInd f θ i d z * Yobs y f θ j z) := by ring
_ = y i d * (expoInd f θ j d z * expoInd f θ i d z * y j d) := by rw [h2]
_ = (y i d * y j d) * (expoInd f θ i d z * expoInd f θ j d z) := by ring
rw [hprod]
rw [div_mul_eq_mul_div, div_mul_eq_mul_div, div_mul_eq_mul_div, mul_div_assoc,
mul_div_assoc, mul_div_assoc, div_self hjoint_ij]
ring
theorem E_htCovEst_le reviewed
⚑ flagged
Causalean.Experimentation.ExposureMappingInterference
Proposition 5.4 (ncov). Suppose the two treatment arms dk, dl are distinct, every unit has nonzero exposure propensity under dk and under dl, and every off-diagonal pair has nonzero cross-arm joint exposure propensity — the positive marginal and positive cross-joint regime. Then the Horvitz–Thompson covariance estimator is nonpositively biased for the true design covariance of the two HT totals.
Formal statement
hypotheses
Ω :
Type u_1
ι :
Type u_2
Θ :
Type u_3
Δ :
Type u_4
D :
y :
ι → Δ → ℝ
f :
Ω → Θ → Δ
θ :
ι → Θ
dk dl :
Δ
hne :
dk ≠ dl
hk :
∀ i, prop D f θ i dk ≠ 0
hl :
∀ i, prop D f θ i dl ≠ 0
hjoint :
∀ i j
if
i ≠ j
then
propPairCross D f θ i j dk dl ≠ 0
Proof (Lean source)
theorem E_htCovEst_le (D : FiniteDesign Ω) (y : ι → Δ → ℝ) (f : Ω → Θ → Δ) (θ : ι → Θ)
(dk dl : Δ) (hne : dk ≠ dl) (hk : ∀ i, prop D f θ i dk ≠ 0) (hl : ∀ i, prop D f θ i dl ≠ 0)
(hjoint : ∀ i j, i ≠ j → propPairCross D f θ i j dk dl ≠ 0) :
D.E (htCovEst D y f θ dk dl) ≤ D.Cov (htTotal D y f θ dk) (htTotal D y f θ dl) := by
rw [Cov_htTotal D y f θ dk dl hne hk hl]
-- compute the expectation of the covariance estimator
have hE : D.E (htCovEst D y f θ dk dl)
= (∑ i, ∑ j ∈ Finset.univ.erase i,
(propPairCross D f θ i j dk dl - prop D f θ i dk * prop D f θ j dl)
* ((y i dk / prop D f θ i dk) * (y j dl / prop D f θ j dl)))
- ∑ i, ((y i dk) ^ 2 / 2 + (y i dl) ^ 2 / 2) := by
unfold htCovEst
rw [D.E_sub, D.E_sum, D.E_sum]
congr 1
· -- off-diagonal sum (same cancellation as the variance case)
refine Finset.sum_congr rfl (fun i _ => ?_)
rw [D.E_sum]
refine Finset.sum_congr rfl (fun j hj => ?_)
have hji : j ≠ i := (Finset.mem_erase.mp hj).1
have hij : i ≠ j := fun h => hji h.symm
have hjoint_ij := hjoint i j hij
have hidk := hk i
have hjdl := hl j
have key : (fun z => expoInd f θ i dk z * expoInd f θ j dl z
* ((propPairCross D f θ i j dk dl - prop D f θ i dk * prop D f θ j dl)
/ propPairCross D f θ i j dk dl)
* ((Yobs y f θ i z / prop D f θ i dk) * (Yobs y f θ j z / prop D f θ j dl)))
= (fun z => (((propPairCross D f θ i j dk dl - prop D f θ i dk * prop D f θ j dl)
/ propPairCross D f θ i j dk dl)
* ((prop D f θ i dk)⁻¹ * (prop D f θ j dl)⁻¹))
* (expoInd f θ i dk z * expoInd f θ j dl z
* Yobs y f θ i z * Yobs y f θ j z)) := by
funext z; ring
rw [key, D.E_const_mul]
have hprod : D.E (fun z => expoInd f θ i dk z * expoInd f θ j dl z
* Yobs y f θ i z * Yobs y f θ j z)
= y i dk * y j dl * propPairCross D f θ i j dk dl := by
rw [D.E_congr (Y := fun z => (y i dk * y j dl)
* (expoInd f θ i dk z * expoInd f θ j dl z)) (fun z => ?_)]
· rw [D.E_const_mul]; rfl
· have h1 : expoInd f θ i dk z * expoInd f θ j dl z * Yobs y f θ i z
= expoInd f θ i dk z * expoInd f θ j dl z * y i dk :=
expoInd₂_mul_Yobs y f θ i dk (expoInd f θ j dl) z
have h2 : expoInd f θ j dl z * expoInd f θ i dk z * Yobs y f θ j z
= expoInd f θ j dl z * expoInd f θ i dk z * y j dl :=
expoInd₂_mul_Yobs y f θ j dl (expoInd f θ i dk) z
calc expoInd f θ i dk z * expoInd f θ j dl z * Yobs y f θ i z * Yobs y f θ j z
= (expoInd f θ i dk z * expoInd f θ j dl z * Yobs y f θ i z) * Yobs y f θ j z := by
ring
_ = (expoInd f θ i dk z * expoInd f θ j dl z * y i dk) * Yobs y f θ j z := by rw [h1]
_ = y i dk * (expoInd f θ j dl z * expoInd f θ i dk z * Yobs y f θ j z) := by ring
_ = y i dk * (expoInd f θ j dl z * expoInd f θ i dk z * y j dl) := by rw [h2]
_ = (y i dk * y j dl) * (expoInd f θ i dk z * expoInd f θ j dl z) := by ring
rw [hprod]
rw [div_mul_eq_mul_div, div_mul_eq_mul_div, div_mul_eq_mul_div, mul_div_assoc,
mul_div_assoc, mul_div_assoc, div_self hjoint_ij]
ring
· -- diagonal correction sum
refine Finset.sum_congr rfl (fun i _ => ?_)
rw [D.E_add]
have hidk := hk i
have hidl := hl i
congr 1
· have key : (fun z => expoInd f θ i dk z * (Yobs y f θ i z) ^ 2 / (2 * prop D f θ i dk))
= (fun z => (2 * prop D f θ i dk)⁻¹ * (expoInd f θ i dk z * (Yobs y f θ i z) ^ 2)) := by
funext z; rw [div_eq_mul_inv]; ring
rw [key, D.E_const_mul,
D.E_congr (Y := fun z => expoInd f θ i dk z * (y i dk) ^ 2)
(fun z => expoInd_mul_Yobs_sq y f θ i dk z),
D.E_mul_const, E_expoInd]
rw [mul_inv,
show (2:ℝ)⁻¹ * (prop D f θ i dk)⁻¹ * (prop D f θ i dk * y i dk ^ 2)
= (2:ℝ)⁻¹ * y i dk ^ 2 * ((prop D f θ i dk)⁻¹ * prop D f θ i dk) by ring,
inv_mul_cancel₀ hidk]
ring
· have key : (fun z => expoInd f θ i dl z * (Yobs y f θ i z) ^ 2 / (2 * prop D f θ i dl))
= (fun z => (2 * prop D f θ i dl)⁻¹ * (expoInd f θ i dl z * (Yobs y f θ i z) ^ 2)) := by
funext z; rw [div_eq_mul_inv]; ring
rw [key, D.E_const_mul,
D.E_congr (Y := fun z => expoInd f θ i dl z * (y i dl) ^ 2)
(fun z => expoInd_mul_Yobs_sq y f θ i dl z),
D.E_mul_const, E_expoInd]
rw [mul_inv,
show (2:ℝ)⁻¹ * (prop D f θ i dl)⁻¹ * (prop D f θ i dl * y i dl ^ 2)
= (2:ℝ)⁻¹ * y i dl ^ 2 * ((prop D f θ i dl)⁻¹ * prop D f θ i dl) by ring,
inv_mul_cancel₀ hidl]
ring
rw [hE]
-- now: offdiag - ∑ ((y dk)²+(y dl)²)/2 ≤ offdiag - ∑ y dk y dl
apply sub_le_sub_left
-- ∑ y dk y dl ≤ ∑ ((y dk)²+(y dl)²)/2, termwise via Young's inequality
rw [← sub_nonneg, ← Finset.sum_sub_distrib]
refine Finset.sum_induction _ (0 ≤ ·) (fun a b ha hb => add_nonneg ha hb) le_rfl
(fun i _ => ?_)
rw [show y i dk ^ 2 / 2 + y i dl ^ 2 / 2 - y i dk * y i dl = (y i dk - y i dl) ^ 2 / 2 by ring]
exact div_nonneg (sq_nonneg _) (by norm_num)
theorem E_htEffectVarEst_ge reviewed
⚑ flagged
Causalean.Experimentation.ExposureMappingInterference
Proposition 5.7 (consvar). Suppose the two treatment arms dk, dl are distinct, every unit has nonzero exposure propensity under dk and under dl, and every off-diagonal pair has nonzero same-arm joint exposure propensity under dk and under dl, as well as nonzero cross-arm joint exposure propensity — the positive-joint regime. Then the assembled Horvitz–Thompson effect-variance estimator has nonnegative bias: its expectation is at least the true design variance of the effect estimator τ̂.
Formal statement
hypotheses
Ω :
Type u_1
ι :
Type u_2
Θ :
Type u_3
Δ :
Type u_4
D :
y :
ι → Δ → ℝ
f :
Ω → Θ → Δ
θ :
ι → Θ
dk dl :
Δ
hne :
dk ≠ dl
hk :
∀ i, prop D f θ i dk ≠ 0
hl :
∀ i, prop D f θ i dl ≠ 0
hjk :
∀ i j
if
i ≠ j
then
propPairSame D f θ i j dk ≠ 0
hjl :
∀ i j
if
i ≠ j
then
propPairSame D f θ i j dl ≠ 0
hjc :
∀ i j
if
i ≠ j
then
propPairCross D f θ i j dk dl ≠ 0
conclusion
D.Var (htEffect D y f θ dk dl) ≤ D.E (htEffectVarEst D y f θ dk dl)
Proof (Lean source)
theorem E_htEffectVarEst_ge (D : FiniteDesign Ω) (y : ι → Δ → ℝ) (f : Ω → Θ → Δ) (θ : ι → Θ)
(dk dl : Δ) (hne : dk ≠ dl)
(hk : ∀ i, prop D f θ i dk ≠ 0) (hl : ∀ i, prop D f θ i dl ≠ 0)
(hjk : ∀ i j, i ≠ j → propPairSame D f θ i j dk ≠ 0)
(hjl : ∀ i j, i ≠ j → propPairSame D f θ i j dl ≠ 0)
(hjc : ∀ i j, i ≠ j → propPairCross D f θ i j dk dl ≠ 0) :
D.Var (htEffect D y f θ dk dl) ≤ D.E (htEffectVarEst D y f θ dk dl) := by
set N : ℝ := (Fintype.card ι : ℝ) with hN
-- Variance of the effect estimator in terms of the totals' moments.
have hVar : D.Var (htEffect D y f θ dk dl)
= N⁻¹ ^ 2 * (D.Var (htTotal D y f θ dk) + D.Var (htTotal D y f θ dl)
- 2 * D.Cov (htTotal D y f θ dk) (htTotal D y f θ dl)) := by
rw [D.Var_congr (Y := fun z => N⁻¹ * (htTotal D y f θ dk z - htTotal D y f θ dl z))
(fun z => by unfold htEffect htMean; rw [div_eq_mul_inv, div_eq_mul_inv]; ring)]
rw [FiniteDesign.Var_const_mul, FiniteDesign.Var_sub]
-- Expectation of the variance estimator in terms of the same moments.
have hE : D.E (htEffectVarEst D y f θ dk dl)
= N⁻¹ ^ 2 * (D.Var (htTotal D y f θ dk) + D.Var (htTotal D y f θ dl)
- 2 * D.E (htCovEst D y f θ dk dl)) := by
rw [D.E_congr (Y := fun z => N⁻¹ ^ 2 *
(htVarEst D y f θ dk z + htVarEst D y f θ dl z - 2 * htCovEst D y f θ dk dl z))
(fun z => by unfold htEffectVarEst; rw [div_eq_mul_inv, mul_comm, ← hN]; ring)]
rw [D.E_const_mul]
congr 1
rw [D.E_sub, D.E_add, D.E_const_mul,
E_htVarEst D y f θ dk hk hjk, E_htVarEst D y f θ dl hl hjl]
rw [hVar, hE]
-- N⁻² ≥ 0, and E[htCovEst] ≤ Cov gives the bracket inequality.
have hCov := E_htCovEst_le D y f θ dk dl hne hk hl hjc
have hsq : (0 : ℝ) ≤ N⁻¹ ^ 2 := sq_nonneg _
apply mul_le_mul_of_nonneg_left _ hsq
-- a + b - 2 Cov ≤ a + b - 2 E[htCovEst] since E[htCovEst] ≤ Cov
have h2 : (2 : ℝ) * D.E (htCovEst D y f θ dk dl)
≤ 2 * D.Cov (htTotal D y f θ dk) (htTotal D y f θ dl) := by
apply mul_le_mul_of_nonneg_left hCov
norm_num
exact sub_le_sub_left h2 (D.Var (htTotal D y f θ dk) + D.Var (htTotal D y f θ dl))
theorem E_htVarEst_eq_addBias reviewed
⚑ flagged
Causalean.Experimentation.ExposureMappingInterference
Proposition 5.2 (varbias). Assuming only every unit has nonzero exposure propensity under d — without the positive-joint assumption — the expectation of the Horvitz–Thompson variance estimator equals the true design variance of the HT total plus the signed zero-joint correction A = ∑_{π_{ij}(d)=0} y_i(d)·y_j(d).
Formal statement
hypotheses
Ω :
Type u_1
ι :
Type u_2
Θ :
Type u_3
Δ :
Type u_4
D :
y :
ι → Δ → ℝ
f :
Ω → Θ → Δ
θ :
ι → Θ
d :
Δ
hpos :
∀ i, prop D f θ i d ≠ 0
conclusion
D.E (htVarEst D y f θ d)
= D.Var (htTotal D y f θ d)
+ ∑ i, ∑ j ∈ Finset.univ.erase i, (if propPairSame D f θ i j d = 0 then (1:ℝ) else 0) * (y i d * y j d)
Proof (Lean source)
theorem E_htVarEst_eq_addBias (D : FiniteDesign Ω) (y : ι → Δ → ℝ) (f : Ω → Θ → Δ) (θ : ι → Θ)
(d : Δ) (hpos : ∀ i, prop D f θ i d ≠ 0) :
D.E (htVarEst D y f θ d)
= D.Var (htTotal D y f θ d)
+ ∑ i, ∑ j ∈ Finset.univ.erase i,
(if propPairSame D f θ i j d = 0 then (1:ℝ) else 0) * (y i d * y j d) := by
rw [Var_htTotal]
unfold htVarEst
rw [D.E_add, D.E_sum, D.E_sum]
-- The diagonal sums coincide; the off-diagonal sums differ by the zero-joint correction.
rw [add_assoc]
congr 1
· -- diagonal sum (identical to `E_htVarEst`)
refine Finset.sum_congr rfl (fun i _ => ?_)
have key : (fun z => expoInd f θ i d z * (1 - prop D f θ i d)
* (Yobs y f θ i z / prop D f θ i d) ^ 2)
= (fun z => ((1 - prop D f θ i d) * (prop D f θ i d)⁻¹ ^ 2)
* (expoInd f θ i d z * (Yobs y f θ i z) ^ 2)) := by
funext z; rw [div_pow]; ring
rw [key, D.E_const_mul]
rw [D.E_congr (Y := fun z => expoInd f θ i d z * (y i d) ^ 2)
(fun z => expoInd_mul_Yobs_sq y f θ i d z)]
rw [D.E_mul_const, E_expoInd]
ring
· -- off-diagonal: combine the estimator's off-diagonal expectation with the bias term,
-- and show the result equals the true off-diagonal sum.
rw [← Finset.sum_add_distrib]
refine Finset.sum_congr rfl (fun i _ => ?_)
rw [D.E_sum, ← Finset.sum_add_distrib]
refine Finset.sum_congr rfl (fun j hj => ?_)
have hji : j ≠ i := (Finset.mem_erase.mp hj).1
have hij : i ≠ j := fun h => hji h.symm
have hi := hpos i
have hjp := hpos j
-- compute the expectation of the estimator's off-diagonal term
have key : (fun z => expoInd f θ i d z * expoInd f θ j d z
* ((propPairSame D f θ i j d - prop D f θ i d * prop D f θ j d)
/ propPairSame D f θ i j d)
* ((Yobs y f θ i z / prop D f θ i d) * (Yobs y f θ j z / prop D f θ j d)))
= (fun z => (((propPairSame D f θ i j d - prop D f θ i d * prop D f θ j d)
/ propPairSame D f θ i j d) * ((prop D f θ i d)⁻¹ * (prop D f θ j d)⁻¹))
* (expoInd f θ i d z * expoInd f θ j d z
* Yobs y f θ i z * Yobs y f θ j z)) := by
funext z; ring
rw [key, D.E_const_mul]
have hprod : D.E (fun z => expoInd f θ i d z * expoInd f θ j d z
* Yobs y f θ i z * Yobs y f θ j z)
= y i d * y j d * propPairSame D f θ i j d := by
rw [D.E_congr (Y := fun z => (y i d * y j d)
* (expoInd f θ i d z * expoInd f θ j d z)) (fun z => ?_)]
· rw [D.E_const_mul]; rfl
· have h1 : expoInd f θ i d z * expoInd f θ j d z * Yobs y f θ i z
= expoInd f θ i d z * expoInd f θ j d z * y i d :=
expoInd₂_mul_Yobs y f θ i d (expoInd f θ j d) z
have h2 : expoInd f θ j d z * expoInd f θ i d z * Yobs y f θ j z
= expoInd f θ j d z * expoInd f θ i d z * y j d :=
expoInd₂_mul_Yobs y f θ j d (expoInd f θ i d) z
calc expoInd f θ i d z * expoInd f θ j d z * Yobs y f θ i z * Yobs y f θ j z
= (expoInd f θ i d z * expoInd f θ j d z * Yobs y f θ i z) * Yobs y f θ j z := by ring
_ = (expoInd f θ i d z * expoInd f θ j d z * y i d) * Yobs y f θ j z := by rw [h1]
_ = y i d * (expoInd f θ j d z * expoInd f θ i d z * Yobs y f θ j z) := by ring
_ = y i d * (expoInd f θ j d z * expoInd f θ i d z * y j d) := by rw [h2]
_ = (y i d * y j d) * (expoInd f θ i d z * expoInd f θ j d z) := by ring
rw [hprod]
-- now case split on whether the joint probability vanishes
by_cases hz : propPairSame D f θ i j d = 0
· rw [if_pos hz, hz]
-- estimator term vanishes (the `* (yᵢyⱼ * 0)` factor); true off-diagonal term is `−yᵢyⱼ`,
-- the bias term `+ yᵢyⱼ` exactly cancels it, both sides equal `0`.
rw [mul_zero, mul_zero, zero_sub, one_mul]
field_simp
ring
· rw [if_neg hz, zero_mul, add_zero]
-- estimator term equals true off-diagonal term (positive-joint cancellation)
rw [div_mul_eq_mul_div, div_mul_eq_mul_div, div_mul_eq_mul_div, mul_div_assoc,
mul_div_assoc, mul_div_assoc, div_self hz]
ring
def htA2 reviewed
⚑ flagged
Causalean.Experimentation.ExposureMappingInterference
For a finite assignment space, a finite unit population, a unit-trait space, an exposure space, a randomization design, exposure-indexed potential outcomes, an exposure mapping, unit traits, an exposure level, and an assignment, the Young-inequality correction adds, over every ordered pair of distinct units with zero same-exposure joint probability, the two corresponding half-weighted squared observed-outcome terms.
Definition (Lean source)
parameters
Ω :
Type u_1
ι :
Type u_2
Θ :
Type u_3
Δ :
Type u_4
D :
y :
ι → Δ → ℝ
f :
Ω → Θ → Δ
θ :
ι → Θ
d :
Δ
z :
Ω
def
htA2 D y f θ d z :
ℝ
theorem E_htVarEst_add_htA2_ge reviewed
⚑ flagged
Causalean.Experimentation.ExposureMappingInterference
Proposition 5.3 (a2). Assuming only every unit has nonzero exposure propensity under d, adding the Young correction Â₂ to the Horvitz–Thompson variance estimator makes it conservative for the true design variance of the HT total: Var[ŷᵀ(d)] ≤ E[V̂ + Â₂].
Formal statement
hypotheses
Ω :
Type u_1
ι :
Type u_2
Θ :
Type u_3
Δ :
Type u_4
D :
y :
ι → Δ → ℝ
f :
Ω → Θ → Δ
θ :
ι → Θ
d :
Δ
hpos :
∀ i, prop D f θ i d ≠ 0
Proof (Lean source)
theorem E_htVarEst_add_htA2_ge (D : FiniteDesign Ω) (y : ι → Δ → ℝ) (f : Ω → Θ → Δ) (θ : ι → Θ)
(d : Δ) (hpos : ∀ i, prop D f θ i d ≠ 0) :
D.Var (htTotal D y f θ d)
≤ D.E (fun z => htVarEst D y f θ d z + htA2 D y f θ d z) := by
rw [D.E_add, E_htVarEst_eq_addBias D y f θ d hpos]
-- E[Â₂] = ∑_{π_{ij}=0} (y_i²/2 + y_j²/2)
have hEA2 : D.E (htA2 D y f θ d)
= ∑ i, ∑ j ∈ Finset.univ.erase i,
(if propPairSame D f θ i j d = 0 then (1:ℝ) else 0)
* ((y i d) ^ 2 / 2 + (y j d) ^ 2 / 2) := by
unfold htA2
rw [D.E_sum]
refine Finset.sum_congr rfl (fun i _ => ?_)
rw [D.E_sum]
refine Finset.sum_congr rfl (fun j hj => ?_)
have hi := hpos i
have hjp := hpos j
have key : (fun z => (if propPairSame D f θ i j d = 0 then (1:ℝ) else 0)
* (expoInd f θ i d z * (Yobs y f θ i z) ^ 2 / (2 * prop D f θ i d)
+ expoInd f θ j d z * (Yobs y f θ j z) ^ 2 / (2 * prop D f θ j d)))
= (fun z => (if propPairSame D f θ i j d = 0 then (1:ℝ) else 0)
* ((2 * prop D f θ i d)⁻¹ * (expoInd f θ i d z * (Yobs y f θ i z) ^ 2)
+ (2 * prop D f θ j d)⁻¹ * (expoInd f θ j d z * (Yobs y f θ j z) ^ 2))) := by
funext z; rw [div_eq_mul_inv, div_eq_mul_inv]; ring
rw [key, D.E_const_mul, D.E_add, D.E_const_mul, D.E_const_mul,
D.E_congr (Y := fun z => expoInd f θ i d z * (y i d) ^ 2)
(fun z => expoInd_mul_Yobs_sq y f θ i d z),
D.E_congr (X := fun z => expoInd f θ j d z * (Yobs y f θ j z) ^ 2)
(Y := fun z => expoInd f θ j d z * (y j d) ^ 2)
(fun z => expoInd_mul_Yobs_sq y f θ j d z),
D.E_mul_const, D.E_mul_const, E_expoInd, E_expoInd]
rw [mul_inv, mul_inv]
congr 1
rw [show (2:ℝ)⁻¹ * (prop D f θ i d)⁻¹ * (prop D f θ i d * y i d ^ 2)
= (2:ℝ)⁻¹ * y i d ^ 2 * ((prop D f θ i d)⁻¹ * prop D f θ i d) by ring,
inv_mul_cancel₀ hi,
show (2:ℝ)⁻¹ * (prop D f θ j d)⁻¹ * (prop D f θ j d * y j d ^ 2)
= (2:ℝ)⁻¹ * y j d ^ 2 * ((prop D f θ j d)⁻¹ * prop D f θ j d) by ring,
inv_mul_cancel₀ hjp]
ring
rw [hEA2]
-- Var + A + E[Â₂] = Var + ∑_{zero}(y_i+y_j)²/2 ≥ Var
rw [add_assoc]
apply le_add_of_nonneg_right
rw [← Finset.sum_add_distrib]
refine sum_nonneg (fun i _ => ?_)
rw [← Finset.sum_add_distrib]
refine sum_nonneg (fun j _ => ?_)
by_cases hz : propPairSame D f θ i j d = 0
· rw [if_pos hz, one_mul, one_mul,
show y i d * y j d + (y i d ^ 2 / 2 + y j d ^ 2 / 2) = (y i d + y j d) ^ 2 / 2 by ring]
exact div_nonneg (sq_nonneg _) (by norm_num)
· rw [if_neg hz, zero_mul, zero_mul, add_zero]
theorem E_htCovEst_eq_of_noEffect reviewed
⚑ flagged
Causalean.Experimentation.ExposureMappingInterference
Proposition 5.5 (no_bias_cov). Suppose the two treatment arms dk, dl are distinct, every unit has nonzero exposure propensity under dk and under dl, every off-diagonal pair has nonzero cross-arm joint exposure propensity — the positive marginal and positive cross-joint regime — and the two exposures share the same potential outcomes: y_i(dk) = y_i(dl) for every unit i. Then the Horvitz–Thompson covariance estimator is exactly unbiased for the true design covariance of the two HT totals: the Young diagonal correction (y_i²/2 + y_i²/2) = y_i² = y_i(dk)y_i(dl) is exact, so the nonpositive bias of Proposition 5.4 vanishes.
Formal statement
hypotheses
Ω :
Type u_1
ι :
Type u_2
Θ :
Type u_3
Δ :
Type u_4
D :
y :
ι → Δ → ℝ
f :
Ω → Θ → Δ
θ :
ι → Θ
dk dl :
Δ
hne :
dk ≠ dl
hk :
∀ i, prop D f θ i dk ≠ 0
hl :
∀ i, prop D f θ i dl ≠ 0
hjc :
∀ i j
if
i ≠ j
then
propPairCross D f θ i j dk dl ≠ 0
heq :
∀ i, y i dk = y i dl
Proof (Lean source)
theorem E_htCovEst_eq_of_noEffect (D : FiniteDesign Ω) (y : ι → Δ → ℝ) (f : Ω → Θ → Δ)
(θ : ι → Θ) (dk dl : Δ) (hne : dk ≠ dl)
(hk : ∀ i, prop D f θ i dk ≠ 0) (hl : ∀ i, prop D f θ i dl ≠ 0)
(hjc : ∀ i j, i ≠ j → propPairCross D f θ i j dk dl ≠ 0)
(heq : ∀ i, y i dk = y i dl) :
D.E (htCovEst D y f θ dk dl) = D.Cov (htTotal D y f θ dk) (htTotal D y f θ dl) := by
rw [Cov_htTotal D y f θ dk dl hne hk hl]
have hE : D.E (htCovEst D y f θ dk dl)
= (∑ i, ∑ j ∈ Finset.univ.erase i,
(propPairCross D f θ i j dk dl - prop D f θ i dk * prop D f θ j dl)
* ((y i dk / prop D f θ i dk) * (y j dl / prop D f θ j dl)))
- ∑ i, ((y i dk) ^ 2 / 2 + (y i dl) ^ 2 / 2) := by
unfold htCovEst
rw [D.E_sub, D.E_sum, D.E_sum]
congr 1
· refine Finset.sum_congr rfl (fun i _ => ?_)
rw [D.E_sum]
refine Finset.sum_congr rfl (fun j hj => ?_)
have hji : j ≠ i := (Finset.mem_erase.mp hj).1
have hij : i ≠ j := fun h => hji h.symm
have hjoint_ij := hjc i j hij
have hidk := hk i
have hjdl := hl j
have key : (fun z => expoInd f θ i dk z * expoInd f θ j dl z
* ((propPairCross D f θ i j dk dl - prop D f θ i dk * prop D f θ j dl)
/ propPairCross D f θ i j dk dl)
* ((Yobs y f θ i z / prop D f θ i dk) * (Yobs y f θ j z / prop D f θ j dl)))
= (fun z => (((propPairCross D f θ i j dk dl - prop D f θ i dk * prop D f θ j dl)
/ propPairCross D f θ i j dk dl)
* ((prop D f θ i dk)⁻¹ * (prop D f θ j dl)⁻¹))
* (expoInd f θ i dk z * expoInd f θ j dl z
* Yobs y f θ i z * Yobs y f θ j z)) := by
funext z; ring
rw [key, D.E_const_mul]
have hprod : D.E (fun z => expoInd f θ i dk z * expoInd f θ j dl z
* Yobs y f θ i z * Yobs y f θ j z)
= y i dk * y j dl * propPairCross D f θ i j dk dl := by
rw [D.E_congr (Y := fun z => (y i dk * y j dl)
* (expoInd f θ i dk z * expoInd f θ j dl z)) (fun z => ?_)]
· rw [D.E_const_mul]; rfl
· have h1 : expoInd f θ i dk z * expoInd f θ j dl z * Yobs y f θ i z
= expoInd f θ i dk z * expoInd f θ j dl z * y i dk :=
expoInd₂_mul_Yobs y f θ i dk (expoInd f θ j dl) z
have h2 : expoInd f θ j dl z * expoInd f θ i dk z * Yobs y f θ j z
= expoInd f θ j dl z * expoInd f θ i dk z * y j dl :=
expoInd₂_mul_Yobs y f θ j dl (expoInd f θ i dk) z
calc expoInd f θ i dk z * expoInd f θ j dl z * Yobs y f θ i z * Yobs y f θ j z
= (expoInd f θ i dk z * expoInd f θ j dl z * Yobs y f θ i z) * Yobs y f θ j z := by
ring
_ = (expoInd f θ i dk z * expoInd f θ j dl z * y i dk) * Yobs y f θ j z := by rw [h1]
_ = y i dk * (expoInd f θ j dl z * expoInd f θ i dk z * Yobs y f θ j z) := by ring
_ = y i dk * (expoInd f θ j dl z * expoInd f θ i dk z * y j dl) := by rw [h2]
_ = (y i dk * y j dl) * (expoInd f θ i dk z * expoInd f θ j dl z) := by ring
rw [hprod]
rw [div_mul_eq_mul_div, div_mul_eq_mul_div, div_mul_eq_mul_div, mul_div_assoc,
mul_div_assoc, mul_div_assoc, div_self hjoint_ij]
ring
· refine Finset.sum_congr rfl (fun i _ => ?_)
rw [D.E_add]
have hidk := hk i
have hidl := hl i
congr 1
· have key : (fun z => expoInd f θ i dk z * (Yobs y f θ i z) ^ 2 / (2 * prop D f θ i dk))
= (fun z => (2 * prop D f θ i dk)⁻¹ * (expoInd f θ i dk z * (Yobs y f θ i z) ^ 2)) := by
funext z; rw [div_eq_mul_inv]; ring
rw [key, D.E_const_mul,
D.E_congr (Y := fun z => expoInd f θ i dk z * (y i dk) ^ 2)
(fun z => expoInd_mul_Yobs_sq y f θ i dk z),
D.E_mul_const, E_expoInd]
rw [mul_inv,
show (2:ℝ)⁻¹ * (prop D f θ i dk)⁻¹ * (prop D f θ i dk * y i dk ^ 2)
= (2:ℝ)⁻¹ * y i dk ^ 2 * ((prop D f θ i dk)⁻¹ * prop D f θ i dk) by ring,
inv_mul_cancel₀ hidk]
ring
· have key : (fun z => expoInd f θ i dl z * (Yobs y f θ i z) ^ 2 / (2 * prop D f θ i dl))
= (fun z => (2 * prop D f θ i dl)⁻¹ * (expoInd f θ i dl z * (Yobs y f θ i z) ^ 2)) := by
funext z; rw [div_eq_mul_inv]; ring
rw [key, D.E_const_mul,
D.E_congr (Y := fun z => expoInd f θ i dl z * (y i dl) ^ 2)
(fun z => expoInd_mul_Yobs_sq y f θ i dl z),
D.E_mul_const, E_expoInd]
rw [mul_inv,
show (2:ℝ)⁻¹ * (prop D f θ i dl)⁻¹ * (prop D f θ i dl * y i dl ^ 2)
= (2:ℝ)⁻¹ * y i dl ^ 2 * ((prop D f θ i dl)⁻¹ * prop D f θ i dl) by ring,
inv_mul_cancel₀ hidl]
ring
rw [hE]
-- the diagonal correction equals ∑ y_i(dk) y_i(dl) exactly under `heq`
congr 1
refine Finset.sum_congr rfl (fun i _ => ?_)
rw [heq i]; ring
def htCovEstA reviewed
⚑ flagged
Causalean.Experimentation.ExposureMappingInterference
For a finite assignment space, a finite unit population, a unit-trait space, an exposure space, a randomization design, exposure-indexed potential outcomes, an exposure mapping, unit traits, two exposure levels, and an assignment, the general Horvitz--Thompson covariance estimator is its distinct-unit cross-exposure sum minus a Young correction over every ordered pair with zero cross-exposure joint probability.
Definition (Lean source)
parameters
Ω :
Type u_1
ι :
Type u_2
Θ :
Type u_3
Δ :
Type u_4
D :
y :
ι → Δ → ℝ
f :
Ω → Θ → Δ
θ :
ι → Θ
dk dl :
Δ
z :
Ω
def
htCovEstA D y f θ dk dl z :
ℝ
given by
(∑ i, ∑ j ∈ Finset.univ.erase i, expoInd f θ i dk z * expoInd f θ j dl z * ((propPairCross D f θ i j dk dl - prop D f θ i dk * prop D f θ j dl) / propPairCross D f θ i j dk dl) * ((Yobs y f θ i z / prop D f θ i dk) * (Yobs y f θ j z / prop D f θ j dl)))
theorem E_htCovEstA_le reviewed
⚑ flagged
Causalean.Experimentation.ExposureMappingInterference
Proposition 5.6 (cova). Suppose only the two treatment arms dk, dl are distinct and every unit has nonzero exposure propensity under dk and under dl — with NO positive-cross-joint assumption. Then the general Horvitz–Thompson covariance estimator Ĉov_A is nonpositively biased for the true design covariance of the two HT totals: zero-cross-joint off-diagonal pairs drop from the first sum, while the Young correction (summing over every j with π_{ij}(dk,dl)=0, including the diagonal) dominates the corresponding −y_i(dk)y_j(dl) covariance contributions termwise via y_i(dk)y_j(dl) ≤ y_i(dk)²/2 + y_j(dl)²/2.
Formal statement
Proof (Lean source)
theorem E_htCovEstA_le (D : FiniteDesign Ω) (y : ι → Δ → ℝ) (f : Ω → Θ → Δ) (θ : ι → Θ)
(dk dl : Δ) (hne : dk ≠ dl) (hk : ∀ i, prop D f θ i dk ≠ 0) (hl : ∀ i, prop D f θ i dl ≠ 0) :
D.E (htCovEstA D y f θ dk dl) ≤ D.Cov (htTotal D y f θ dk) (htTotal D y f θ dl) := by
rw [Cov_htTotal D y f θ dk dl hne hk hl]
-- expectation of the general covariance estimator
have hE : D.E (htCovEstA D y f θ dk dl)
= (∑ i, ∑ j ∈ Finset.univ.erase i,
(if propPairCross D f θ i j dk dl = 0 then (0:ℝ) else 1)
* ((propPairCross D f θ i j dk dl - prop D f θ i dk * prop D f θ j dl)
* ((y i dk / prop D f θ i dk) * (y j dl / prop D f θ j dl))))
- ∑ i, ∑ j, (if propPairCross D f θ i j dk dl = 0 then (1:ℝ) else 0)
* ((y i dk) ^ 2 / 2 + (y j dl) ^ 2 / 2) := by
unfold htCovEstA
rw [D.E_sub, D.E_sum, D.E_sum]
congr 1
· -- first (off-diagonal) sum
refine Finset.sum_congr rfl (fun i _ => ?_)
rw [D.E_sum]
refine Finset.sum_congr rfl (fun j hj => ?_)
have hji : j ≠ i := (Finset.mem_erase.mp hj).1
have hij : i ≠ j := fun h => hji h.symm
have hidk := hk i
have hjdl := hl j
have key : (fun z => expoInd f θ i dk z * expoInd f θ j dl z
* ((propPairCross D f θ i j dk dl - prop D f θ i dk * prop D f θ j dl)
/ propPairCross D f θ i j dk dl)
* ((Yobs y f θ i z / prop D f θ i dk) * (Yobs y f θ j z / prop D f θ j dl)))
= (fun z => (((propPairCross D f θ i j dk dl - prop D f θ i dk * prop D f θ j dl)
/ propPairCross D f θ i j dk dl)
* ((prop D f θ i dk)⁻¹ * (prop D f θ j dl)⁻¹))
* (expoInd f θ i dk z * expoInd f θ j dl z
* Yobs y f θ i z * Yobs y f θ j z)) := by
funext z; ring
rw [key, D.E_const_mul]
have hprod : D.E (fun z => expoInd f θ i dk z * expoInd f θ j dl z
* Yobs y f θ i z * Yobs y f θ j z)
= y i dk * y j dl * propPairCross D f θ i j dk dl := by
rw [D.E_congr (Y := fun z => (y i dk * y j dl)
* (expoInd f θ i dk z * expoInd f θ j dl z)) (fun z => ?_)]
· rw [D.E_const_mul]; rfl
· have h1 : expoInd f θ i dk z * expoInd f θ j dl z * Yobs y f θ i z
= expoInd f θ i dk z * expoInd f θ j dl z * y i dk :=
expoInd₂_mul_Yobs y f θ i dk (expoInd f θ j dl) z
have h2 : expoInd f θ j dl z * expoInd f θ i dk z * Yobs y f θ j z
= expoInd f θ j dl z * expoInd f θ i dk z * y j dl :=
expoInd₂_mul_Yobs y f θ j dl (expoInd f θ i dk) z
calc expoInd f θ i dk z * expoInd f θ j dl z * Yobs y f θ i z * Yobs y f θ j z
= (expoInd f θ i dk z * expoInd f θ j dl z * Yobs y f θ i z) * Yobs y f θ j z := by
ring
_ = (expoInd f θ i dk z * expoInd f θ j dl z * y i dk) * Yobs y f θ j z := by rw [h1]
_ = y i dk * (expoInd f θ j dl z * expoInd f θ i dk z * Yobs y f θ j z) := by ring
_ = y i dk * (expoInd f θ j dl z * expoInd f θ i dk z * y j dl) := by rw [h2]
_ = (y i dk * y j dl) * (expoInd f θ i dk z * expoInd f θ j dl z) := by ring
rw [hprod]
by_cases hz : propPairCross D f θ i j dk dl = 0
· rw [if_pos hz, zero_mul, hz, mul_zero, mul_zero]
· rw [if_neg hz, one_mul]
rw [div_mul_eq_mul_div, div_mul_eq_mul_div, div_mul_eq_mul_div, mul_div_assoc,
mul_div_assoc, mul_div_assoc, div_self hz]
ring
· -- Young correction sum (over all j)
refine Finset.sum_congr rfl (fun i _ => ?_)
rw [D.E_sum]
refine Finset.sum_congr rfl (fun j _ => ?_)
have hidk := hk i
have hjdl := hl j
have key : (fun z => (if propPairCross D f θ i j dk dl = 0 then (1:ℝ) else 0)
* (expoInd f θ i dk z * (Yobs y f θ i z) ^ 2 / (2 * prop D f θ i dk)
+ expoInd f θ j dl z * (Yobs y f θ j z) ^ 2 / (2 * prop D f θ j dl)))
= (fun z => (if propPairCross D f θ i j dk dl = 0 then (1:ℝ) else 0)
* ((2 * prop D f θ i dk)⁻¹ * (expoInd f θ i dk z * (Yobs y f θ i z) ^ 2)
+ (2 * prop D f θ j dl)⁻¹ * (expoInd f θ j dl z * (Yobs y f θ j z) ^ 2))) := by
funext z; rw [div_eq_mul_inv, div_eq_mul_inv]; ring
rw [key, D.E_const_mul, D.E_add, D.E_const_mul, D.E_const_mul,
D.E_congr (Y := fun z => expoInd f θ i dk z * (y i dk) ^ 2)
(fun z => expoInd_mul_Yobs_sq y f θ i dk z),
D.E_congr (X := fun z => expoInd f θ j dl z * (Yobs y f θ j z) ^ 2)
(Y := fun z => expoInd f θ j dl z * (y j dl) ^ 2)
(fun z => expoInd_mul_Yobs_sq y f θ j dl z),
D.E_mul_const, D.E_mul_const, E_expoInd, E_expoInd]
rw [mul_inv, mul_inv]
congr 1
rw [show (2:ℝ)⁻¹ * (prop D f θ i dk)⁻¹ * (prop D f θ i dk * y i dk ^ 2)
= (2:ℝ)⁻¹ * y i dk ^ 2 * ((prop D f θ i dk)⁻¹ * prop D f θ i dk) by ring,
inv_mul_cancel₀ hidk,
show (2:ℝ)⁻¹ * (prop D f θ j dl)⁻¹ * (prop D f θ j dl * y j dl ^ 2)
= (2:ℝ)⁻¹ * y j dl ^ 2 * ((prop D f θ j dl)⁻¹ * prop D f θ j dl) by ring,
inv_mul_cancel₀ hjdl]
ring
rw [hE]
-- Compare to eq:totals_covariance: it suffices to show
-- Cov - E[htCovEstA] = ∑_diag Young-gap + ∑_offdiag Young-gap ≥ 0.
rw [← sub_nonneg]
-- Rewrite the goal's Cov first sum (full off-diagonal) by splitting on π_{ij}=0.
have hsplit : (∑ i, ∑ j ∈ Finset.univ.erase i,
(propPairCross D f θ i j dk dl - prop D f θ i dk * prop D f θ j dl)
* ((y i dk / prop D f θ i dk) * (y j dl / prop D f θ j dl)))
= (∑ i, ∑ j ∈ Finset.univ.erase i,
(if propPairCross D f θ i j dk dl = 0 then (0:ℝ) else 1)
* ((propPairCross D f θ i j dk dl - prop D f θ i dk * prop D f θ j dl)
* ((y i dk / prop D f θ i dk) * (y j dl / prop D f θ j dl))))
+ ∑ i, ∑ j ∈ Finset.univ.erase i,
(if propPairCross D f θ i j dk dl = 0 then (1:ℝ) else 0)
* (- (y i dk * y j dl)) := by
rw [← Finset.sum_add_distrib]
refine Finset.sum_congr rfl (fun i _ => ?_)
rw [← Finset.sum_add_distrib]
refine Finset.sum_congr rfl (fun j hj => ?_)
have hji : j ≠ i := (Finset.mem_erase.mp hj).1
have hidk := hk i
have hjdl := hl j
by_cases hz : propPairCross D f θ i j dk dl = 0
· rw [if_pos hz, if_pos hz, zero_mul, zero_add, one_mul, hz]
field_simp
ring
· rw [if_neg hz, if_neg hz, one_mul, zero_mul, add_zero]
rw [hsplit]
-- Split the all-j Young sum into diagonal (j=i) and off-diagonal (j≠i).
have hYoung : (∑ i, ∑ j, (if propPairCross D f θ i j dk dl = 0 then (1:ℝ) else 0)
* ((y i dk) ^ 2 / 2 + (y j dl) ^ 2 / 2))
= (∑ i, ((y i dk) ^ 2 / 2 + (y i dl) ^ 2 / 2))
+ ∑ i, ∑ j ∈ Finset.univ.erase i,
(if propPairCross D f θ i j dk dl = 0 then (1:ℝ) else 0)
* ((y i dk) ^ 2 / 2 + (y j dl) ^ 2 / 2) := by
rw [← Finset.sum_add_distrib]
refine Finset.sum_congr rfl (fun i _ => ?_)
rw [← Finset.add_sum_erase univ _ (Finset.mem_univ i)]
congr 1
-- diagonal j = i: indicator is 1 since π_{ii}(dk,dl)=0
rw [propPairCross_self_of_ne D f θ i hne, if_pos rfl, one_mul]
rw [hYoung]
-- Name the off-diagonal positive-joint sum `P` (it cancels) and reduce to a gap of
-- nonnegative diagonal + off-diagonal Young corrections.
set P : ℝ := ∑ i, ∑ j ∈ Finset.univ.erase i,
(if propPairCross D f θ i j dk dl = 0 then (0:ℝ) else 1)
* ((propPairCross D f θ i j dk dl - prop D f θ i dk * prop D f θ j dl)
* ((y i dk / prop D f θ i dk) * (y j dl / prop D f θ j dl))) with hP
-- Goal: 0 ≤ (P + Negyy) - (Diagyy + (P - (Diagyoung + Offyoung)))
rw [show P + (∑ i, ∑ j ∈ Finset.univ.erase i,
(if propPairCross D f θ i j dk dl = 0 then (1:ℝ) else 0) * (- (y i dk * y j dl)))
- (∑ i, y i dk * y i dl)
- (P - (∑ i, ((y i dk) ^ 2 / 2 + (y i dl) ^ 2 / 2)
+ ∑ i, ∑ j ∈ Finset.univ.erase i,
(if propPairCross D f θ i j dk dl = 0 then (1:ℝ) else 0)
* ((y i dk) ^ 2 / 2 + (y j dl) ^ 2 / 2)))
= (∑ i, ((y i dk) ^ 2 / 2 + (y i dl) ^ 2 / 2) - ∑ i, y i dk * y i dl)
+ (∑ i, ∑ j ∈ Finset.univ.erase i,
(if propPairCross D f θ i j dk dl = 0 then (1:ℝ) else 0)
* ((y i dk) ^ 2 / 2 + (y j dl) ^ 2 / 2)
+ ∑ i, ∑ j ∈ Finset.univ.erase i,
(if propPairCross D f θ i j dk dl = 0 then (1:ℝ) else 0) * (- (y i dk * y j dl)))
by ring]
apply add_nonneg
· -- diagonal gaps: ∑ᵢ (yᵢdk²/2 + yᵢdl²/2) - ∑ᵢ yᵢdkyᵢdl ≥ 0
rw [← Finset.sum_sub_distrib]
refine sum_nonneg (fun i _ => ?_)
rw [show (y i dk) ^ 2 / 2 + (y i dl) ^ 2 / 2 - y i dk * y i dl
= (y i dk - y i dl) ^ 2 / 2 by ring]
exact div_nonneg (sq_nonneg _) (by norm_num)
· -- off-diagonal gaps
rw [← Finset.sum_add_distrib]
refine sum_nonneg (fun i _ => ?_)
rw [← Finset.sum_add_distrib]
refine sum_nonneg (fun j _ => ?_)
by_cases hz : propPairCross D f θ i j dk dl = 0
· rw [if_pos hz, one_mul, one_mul,
show (y i dk) ^ 2 / 2 + (y j dl) ^ 2 / 2 + - (y i dk * y j dl)
= (y i dk - y j dl) ^ 2 / 2 by ring]
exact div_nonneg (sq_nonneg _) (by norm_num)
· rw [if_neg hz, zero_mul, zero_mul, add_zero]
def htEffectVarEstA reviewed
⚑ flagged
Causalean.Experimentation.ExposureMappingInterference
For a finite assignment space, a finite unit population, a unit-trait space, an exposure space, a randomization design, exposure-indexed potential outcomes, an exposure mapping, unit traits, two exposure levels, and an assignment, the general conservative effect-variance estimator is the two variance estimators and their Young corrections, minus twice the general covariance estimator, divided by the square of the population size.
Definition (Lean source)
parameters
Ω :
Type u_1
ι :
Type u_2
Θ :
Type u_3
Δ :
Type u_4
D :
y :
ι → Δ → ℝ
f :
Ω → Θ → Δ
θ :
ι → Θ
dk dl :
Δ
z :
Ω
def
htEffectVarEstA D y f θ dk dl z :
ℝ
theorem E_htEffectVarEstA_ge reviewed
⚑ flagged
Causalean.Experimentation.ExposureMappingInterference
Proposition 5.7, general form (consvar). Suppose only the two treatment arms dk, dl are distinct and every unit has nonzero exposure propensity under dk and under dl — without any positive-joint assumption. Then the assembled general Horvitz–Thompson effect-variance estimator has nonnegative bias: its expectation is at least the true design variance of the effect estimator τ̂.
Formal statement
hypotheses
Ω :
Type u_1
ι :
Type u_2
Θ :
Type u_3
Δ :
Type u_4
D :
y :
ι → Δ → ℝ
f :
Ω → Θ → Δ
θ :
ι → Θ
dk dl :
Δ
hne :
dk ≠ dl
hk :
∀ i, prop D f θ i dk ≠ 0
hl :
∀ i, prop D f θ i dl ≠ 0
conclusion
D.Var (htEffect D y f θ dk dl) ≤ D.E (htEffectVarEstA D y f θ dk dl)
Proof (Lean source)
theorem E_htEffectVarEstA_ge (D : FiniteDesign Ω) (y : ι → Δ → ℝ) (f : Ω → Θ → Δ) (θ : ι → Θ)
(dk dl : Δ) (hne : dk ≠ dl)
(hk : ∀ i, prop D f θ i dk ≠ 0) (hl : ∀ i, prop D f θ i dl ≠ 0) :
D.Var (htEffect D y f θ dk dl) ≤ D.E (htEffectVarEstA D y f θ dk dl) := by
set N : ℝ := (Fintype.card ι : ℝ) with hN
-- Variance of the effect estimator in terms of the totals' moments.
have hVar : D.Var (htEffect D y f θ dk dl)
= N⁻¹ ^ 2 * (D.Var (htTotal D y f θ dk) + D.Var (htTotal D y f θ dl)
- 2 * D.Cov (htTotal D y f θ dk) (htTotal D y f θ dl)) := by
rw [D.Var_congr (Y := fun z => N⁻¹ * (htTotal D y f θ dk z - htTotal D y f θ dl z))
(fun z => by unfold htEffect htMean; rw [div_eq_mul_inv, div_eq_mul_inv]; ring)]
rw [FiniteDesign.Var_const_mul, FiniteDesign.Var_sub]
-- Expectation of the variance estimator in terms of the corrected moments.
have hE : D.E (htEffectVarEstA D y f θ dk dl)
= N⁻¹ ^ 2 * (D.E (fun z => htVarEst D y f θ dk z + htA2 D y f θ dk z)
+ D.E (fun z => htVarEst D y f θ dl z + htA2 D y f θ dl z)
- 2 * D.E (htCovEstA D y f θ dk dl)) := by
rw [D.E_congr (Y := fun z => N⁻¹ ^ 2 *
((htVarEst D y f θ dk z + htA2 D y f θ dk z)
+ (htVarEst D y f θ dl z + htA2 D y f θ dl z) - 2 * htCovEstA D y f θ dk dl z))
(fun z => by unfold htEffectVarEstA; rw [div_eq_mul_inv, mul_comm, ← hN]; ring)]
rw [D.E_const_mul]
congr 1
rw [D.E_sub, D.E_add, D.E_const_mul]
rw [hVar, hE]
-- N⁻² ≥ 0; Prop 5.3 twice and Prop 5.6 bound the bracket.
have hsq : (0 : ℝ) ≤ N⁻¹ ^ 2 := sq_nonneg _
apply mul_le_mul_of_nonneg_left _ hsq
have hAk := E_htVarEst_add_htA2_ge D y f θ dk hk
have hAl := E_htVarEst_add_htA2_ge D y f θ dl hl
have hCov := E_htCovEstA_le D y f θ dk dl hne hk hl
have h2 : (2 : ℝ) * D.E (htCovEstA D y f θ dk dl)
≤ 2 * D.Cov (htTotal D y f θ dk) (htTotal D y f θ dl) :=
mul_le_mul_of_nonneg_left hCov (by norm_num)
-- Var dk + Var dl - 2 Cov ≤ E[V̂dk+Â₂dk] + E[V̂dl+Â₂dl] - 2 E[Ĉov_A]
exact sub_le_sub (add_le_add hAk hAl) h2