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Econ-67 · economics · Economics

Sharp regret guarantees for dynamic treatment regimes

LLM status Open unverified

Statement

CI-27 |  Econometric theory and causal inference

Research agenda formalization

Definitions and assumptions

Observe $n$ independent longitudinal trajectories with histories $H_t=(\overline L_t,\overline A_{t-1})$, binary decisions $A_t$, and terminal outcome $|Y|\leq1$. Assume consistency, no interference between trajectories, sequential exchangeability, and assignment probabilities bounded between $\varepsilon$ and $1-\varepsilon$. Specify a policy class $\Pi=\prod_{t=1}^T\Pi_t$, where each $\Pi_t$ is pointwise separable and consists of measurable maps $H_t\mapsto\{0,1\}$ with finite Vapnik--Chervonenkis dimension $v_t$. The observational law class $\mathcal P$ additionally restricts transition densities and outcome regressions to stated Hölder balls on history supports of dimensions $D_t$. A policy intervention lets subsequent covariates evolve naturally under its preceding decisions.

Formal question

Write $V_P(\pi)=\E_P[Y^\pi]$ and define the minimax policy regret by \[ \mathfrak R_n= \inf_{\widehat\pi:\,\widehat\pi\in\Pi}\sup_{P\in\mathcal P} \E_P\!\left[\sup_{\pi\in\Pi}V_P(\pi)-V_P(\widehat\pi)\right]. \] Find matching bounds and a rate-attaining learner, with explicit dependence on $T$, $v_t$, $D_t$, nuisance smoothness, and $\varepsilon$. Identify conditions under which the horizon dependence is polynomial, and conditions forcing exponential information loss from insufficient trajectory overlap. Extend the guarantee to increasing horizons only under a stated sequence of model classes and policies.

Scope and status

This is an editorial minimax formulation of a longstanding dynamic-policy agenda, not a claim that dynamic treatment regimes have no existing theory. The supremum permits nonattainment of the optimal value. Per-stage overlap does not automatically yield a horizon-independent guarantee, and estimating a conditional treatment effect at each stage need not by itself maximize terminal value. The policy class, observational sampling mechanism, and continuation behavior are essential parts of the question.

References for the source of the problem

Carlos Cinelli, Avi Feller, Guido Imbens, Edward Kennedy, Sara Magliacane, and Jose Zubizarreta, Challenges in Statistics: A Dozen Challenges in Causality and Causal Inference, arXiv:2508.17099v1 [stat.ME], 23 August 2025, §3.3.3, "New data structures"; §3.3.2 for policy-learning background. https://arxiv.org/html/2508.17099v1#S3.SS3.SSS3.

Common conventions: Source mathematical conventions

Well-defined objects. All probability spaces and measurable variables are specified or inherited from a local statistical model. Expectations used as finite targets require the stated integrability. A decision rule or estimator is measurable with respect to its available information; randomized rules may use an auxiliary independent random variable. Norms without a subscript are Euclidean in finite-dimensional spaces. An optimizer is requested only when attainment is assured; otherwise the question concerns the optimal value and $\varepsilon$-optimal admissible rules for every $\varepsilon>0$.

Identification. A structural model $m\in\mathcal M$ implies an observable law $P_m^{\rm obs}$ and target $\theta(m)$. The target is point identified on $\mathcal M$ if \[ P_{m_1}^{\rm obs}=P_{m_2}^{\rm obs} \quad\Longrightarrow\quad\theta(m_1)=\theta(m_2) \qquad(m_1,m_2\in\mathcal M). \] When this fails, the sharp identified set is \[ \Theta(P;\mathcal M)=\{\theta(m):m\in\mathcal M, P_m^{\rm obs}=P\}. \] Specifying an intervention or estimand does not establish identification. Positivity, exclusion, causal sufficiency, measurement restrictions, and transport assumptions are substantive local inputs, never automatic consequences of notation.

Minimax risk and nontrivial inference. For a statistical experiment with data law $P^{(n)}$, target $\theta(P)$, and nonnegative loss $\ell$, define \[ \mathcal R_n(\mathcal P,\ell) =\inf_{\widehat\theta_n}\sup_{P\in\mathcal P} \E_{P^{(n)}}\ell(\widehat\theta_n,\theta(P)). \] The infimum is over measurable estimators. A sharp rate is a sequence $r_n$ for which $0<c\leq C<\infty$, independent of $n$, satisfies $c r_n\leq\mathcal R_n\leq C r_n$ for all sufficiently large $n$. Squared-error parametric risk is of order $n^{-1}$; the corresponding root-mean-squared error is of order $n^{-1/2}$. These different conventions are distinguished locally. A uniform asymptotic confidence region $C_n$ must satisfy \[ \liminf_{n\to\infty}\inf_{P\in\mathcal P} \Pr_{P^{(n)}}\{\theta(P)\in C_n\}\geq 1-\alpha, \qquad 0<\alpha<1, \] together with an informative diameter or expected-width requirement. Coverage alone permits the vacuous whole parameter space. Testing questions similarly impose both size control and power against a declared separated alternative class.

Binary causal baseline. When an entry explicitly invokes this baseline, one observes independent copies of $O=(X,W,Y)$, with $W\in\{0,1\}$ and potential outcomes $Y(0),Y(1)$. Assume consistency $Y=Y(W)$, no interference, conditional exchangeability $(Y(0),Y(1))\perp W\mid X$, and overlap $\epsilon\leq e(X)\leq1-\epsilon$ almost surely for a fixed $0<\epsilon<1/2$, where \[ e(x)=\Pr(W=1\mid X=x),\qquad m_w(x)=\E[Y\mid W=w,X=x],\qquad \tau(x)=m_1(x)-m_0(x). \] These assumptions identify conditional mean effects almost everywhere under the covariate law. Pointwise or uniform effects require appropriate regularity and a specified support. Continuous treatment, longitudinal data, adaptive experiments, and network interference require their own assumptions in place of this baseline. Bounded outcomes, densities, and smoothness are additional assumptions only where stated.

Function classes. On $[0,1]^d$, $\mathcal H_d^s(L)$ is the isotropic Hölder ball of radius $L>0$: put $k=\lceil s\rceil-1$ and $\gamma=s-k\in(0,1]$, bound derivatives through order $k$, and require the order-$k$ derivatives to be $\gamma$-Hölder with constant at most $L$. Thus integer orders use a Lipschitz final derivative convention. The class $\operatorname{BV}_d(L)$ consists of functions $f\in L^1((0,1)^d)$ whose distributional derivative is a finite vector measure and for which $\|f\|_{L^1}+|Df|((0,1)^d)\leq L$. An $L^\infty$ bound or a particular pointwise version is imposed separately when needed. Sparse and neural-network classes require a declared dictionary or architecture and coefficient bounds.

An anisotropic Hölder class with declared block smoothness indices means coordinatewise Hölder regularity in each block, uniformly in the other blocks, with all corresponding derivative and Hölder bounds at most the stated radius. Derivative orders and the integer-order convention in each block are those just given. Mixed-derivative bounds are additional restrictions only when explicitly stated.

Decision and computational questions. Policy regret compares admissible feasible policies under the same law and constraints. In computational entries, finite data and thresholds have rational encodings; running time is measured in their total bit length. Real-valued equilibrium search uses the explicitly declared algebraic or FIXP encoding. An approximation ratio for maximization is written $\operatorname{OPT}/\operatorname{ALG}$ and for minimization as $\operatorname{ALG}/\operatorname{OPT}$, with zero optima and infeasible instances handled locally. Historical approximation bounds are not automatically current bounds.

Statement licence: CC-BY-4.0 · Source file

Contributions 3

Econ-67-A3 · Exploration · LLM-generated · gpt 6 astra ultra ·

GPT 6 Astra Ultra attempt on Econ-67, version 3

Sharp regret guarantees for dynamic treatment regimes: Matching policy complexity and trajectory-overlap rates

Problem ID: C44-65. Status: Unresolved; rigorous restricted results.

Target and scope

For dynamic deterministic policies $\pi=(\pi_1,\ldots,\pi_T)$ with history-dependent decisions, the canonical target is minimax regret \[ \inf_{\widehat\pi\in\Pi}\sup_{P\in\mathcal P} \mathbb E_P\{\sup_{\pi\in\Pi}V_P(\pi)-V_P(\widehat\pi)\}. \] Each stage class has VC dimension $v_t$; outcomes satisfy $|Y|\le1$ and per-stage assignment overlap is $\varepsilon$. The dynamic-policy agenda appears in [agenda, Section 3.3.3]. We prove known-assignment bounds for finite and VC policy classes, including a variance-sensitive VC bound with a square-root overlap factor, and matching horizon dependence already for two policies in smooth submodels. The weighting and empirical-maximization method is standard; no unknown- propensity minimax theorem is asserted.

Uniform evaluation with known assignment laws

Let the supplied observational assignment probabilities be $q_t(a\mid H_t)\ge\varepsilon$. Set \[ R_\pi(O)=\prod_{t=1}^T\frac{\mathbf1\{A_t=\pi_t(H_t)\}} {q_t(A_t\mid H_t)},\qquad f_\pi=R_\pi Y. \] Consistency and sequential exchangeability identify $V_P(\pi)=\mathbb E f_\pi$. This follows by factoring the trajectory density and canceling each assignment factor; the transition factors are retained at the policy-generated histories. Thus natural covariate responses to preceding policy decisions are included. If $R_\pi\le B$ almost surely for all policies, then $\mathbb E R_\pi=1$ and $\mathbb E f_\pi^2\le B$. The universal choice from per-stage overlap is $B=\varepsilon^{-T}$.
Theorem: For a finite policy class of size $M$, let $\widehat\pi$ maximize $n^{-1}\sum_i f_\pi(O_i)$, or maximize it to deterministic error $\xi$. Then \[ \mathbb E\{\sup_\pi V_P(\pi)-V_P(\widehat\pi)\} \le C\left\{\sqrt{\frac{B\log(2M)}n} +\frac{B\log(2M)}n\right\}+\xi, \tag{1} \] with a universal constant $C$. Regret is also bounded by two.
Proof: For every fixed policy, $|f_\pi-\mathbb Ef_\pi|\le2B$ and $\operatorname{Var}(f_\pi)\le B$. The exponential-moment proof of Bernstein's inequality gives, for $z>0$, \[ P\left\{|(P_n-P)f_\pi|> \sqrt{2Bz/n}+4Bz/(3n)\right\}\le2e^{-z}. \] For completeness its moment bound uses $\mathbb E|Z|^k\le(2B)^{k-2}\mathbb E Z^2$ for centered $Z=f_\pi-\mathbb E f_\pi$, expands $\mathbb E e^{tZ}$, and sums the geometric upper bound on terms $k\ge2$; optimizing Markov's bound gives the displayed inequality. Union bound at $z=\log(2M)+s$ gives failure probability at most $e^{-s}$ for all policies simultaneously. Integrating this tail bounds the expected supremum by a constant times the two terms in (1). Empirical maximality implies regret at most $2\sup_\pi|(P_n-P)f_\pi|+\xi$, proving (1). ∎
Proposition: For the pointwise-separable product class in the canonical statement, let $G_t(n)=\sum_{k=0}^{\min(v_t,n)}\binom nk$ and $G(n)=\prod_t G_t(n)$. An approximate empirical maximizer satisfies \[ \mathbb E\{\sup_\pi V_P(\pi)-V_P(\widehat\pi)\} \le4B\sqrt{\frac{2\log(2G(n))}{n}}+\xi. \tag{2} \] For $1\le v_t\le n$, one may use $G_t(n)\le(en/v_t)^{v_t}$; $G_t(n)=1$ for $v_t=0$. This bound is generally weaker in $B$ than (1).
Proof: On any observed sample, each stage class has at most $G_t(n)$ binary label vectors by the VC growth bound. One elementary proof of that bound uses the recursion $G_v(n)\le G_v(n-1)+G_{v-1}(n-1)$, obtained by splitting labelings according to whether the last coordinate can be both labels; induction yields the binomial sum. The vectors of complete-path indicators therefore number at most $G(n)$. Weights $Y_i\prod_tq_t(A_{it}\mid H_{it})^{-1}$ are fixed on this sample and do not depend on the policy. Each corresponding score vector has Euclidean norm at most $B\sqrt n$. For independent Rademacher signs, the exponential inequality $\mathbb E_\sigma\exp(t\sum_i\sigma_i a_i) \le\exp(t^2\sum_i a_i^2/2)$ and a union bound give an expected absolute maximum at most $B\sqrt{2n\log(2G(n))}$. An independent ghost sample and Jensen's inequality symmetrize $\mathbb E\sup|(P_n-P)f|$ to at most twice that maximum divided by $n$. Pointwise separability makes the suprema measurable. The same empirical- maximization inequality as before proves (2). The standard binomial sum bound follows by expanding $(1+x)^n$ at $x=v_t/n$. ∎

Exponential information loss with one decision to learn

Theorem: For every $T\ge1$ and $0<\varepsilon<1/2$, there is a known-assignment model with $v_1=1$, $v_t=0$ for $t>1$, and smooth continuous-coordinate nuisances of every order, for which minimax regret is \[ \asymp\min\{1,(n\varepsilon^T)^{-1/2}\}. \tag{3} \] The constants can be chosen universal. The policy class has exactly two policies and includes all combinations of its specified stage classes.
Proof: Let assignments be independent Bernoulli$(\varepsilon)$ and histories, if continuous coordinates are required, include independent uniforms irrelevant to outcomes. One policy always assigns one; the other assigns zero at the first stage and one thereafter. Their stage classes are $\Pi_1=\{0,1\}$ and $\Pi_t=\{1\}$ for $t>1$. Let $Y\in\{-1,1\}$ have mean zero off the all-one assignment path and mean $\theta$ on that path. Choose two laws $\theta=\pm\Delta$. Sequential exchangeability holds by independent randomization, and conditional outcome means are constant within each discrete history stratum, satisfying any fixed Hölder index with an adequate radius. The policy values are respectively $\theta$ and zero. Choosing the wrong policy incurs regret $\Delta$. The laws differ only on a path with probability $q=\varepsilon^T$. For $\Delta\le1/2$, one-observation KL is at most $Cq\Delta^2$. Choose $\Delta=b\min\{1,(nq)^{-1/2}\}$ with $b$ sufficiently small. Pinsker's inequality bounds sample total variation by $1/2$; the sum of wrong-policy probabilities is at least $1/2$. Maximal regret is therefore at least $\Delta/4$. For the upper bound, (1) with $M=2$, $B=q^{-1}$ gives $C\{(nq)^{-1/2}+(nq)^{-1}\}$. When $nq\ge1$ the second term is smaller than the first; when $nq<1$ the universal regret bound two applies. This proves (3). ∎

A variance-sensitive bound for the infinite VC product class

The finite-class Bernstein bound (1) scales with $\sqrt B$ in its leading term, whereas the elementary VC bound (2) scales with $B$. The following argument closes that difference for expected regret, still with supplied assignment laws. It does not replace an empirical growth count by an unjustified union bound over random representative policies.

Let $\mathcal F$ be a pointwise separable class of measurable score functions. Define its expected Rademacher average, with an absolute supremum, by \[ \mathcal R_n(\mathcal F) =\mathbb E_{O,\sigma}\sup_{f\in\mathcal F} \left|\frac1n\sum_{i=1}^n\sigma_i f(O_i)\right|, \] where the signs are independent of the sample and of one another. Boundedness and separability allow reduction to a countable determining class, so the suprema and expectations below are measurable.

Lemma (Contraction with an explicit safe constant): For any finite set $A\subset\mathbb R^n$ and maps $\varphi_i$ with $\varphi_i(0)=0$ and Lipschitz constant at most $L$ on the needed coordinate range, \[ \mathbb E_\sigma\sup_{a\in A} \left|\sum_i\sigma_i\varphi_i(a_i)\right| \le2L\,\mathbb E_\sigma\sup_{a\in A} \left|\sum_i\sigma_i a_i\right|. \tag{4} \] The same bound holds for pointwise-separable bounded score vectors.
Proof: First consider a supremum without absolute values. Conditional on the other signs, let $U_a$ denote their contribution. For one coordinate, \[ \frac12\sup_a[U_a+\varphi_i(a_i)] +\frac12\sup_b[U_b-\varphi_i(b_i)] =\frac12\sup_{a,b}[U_a+U_b+\varphi_i(a_i)-\varphi_i(b_i)]. \] Lipschitz continuity bounds this by $\tfrac12\sup_{a,b}[U_a+U_b+L|a_i-b_i|]$. Since the pair $(a,b)$ ranges over all ordered pairs, exchanging it shows that the latter supremum equals $\sup_{a,b}[U_a+U_b+L(a_i-b_i)]$. Dividing by two and separating the suprema gives the sign average with $L a_i$ instead of $\varphi_i(a_i)$. Repeat for every coordinate. Thus nonabsolute Rademacher suprema contract with factor $L$.

Adjoin the zero vector to $A$; this does not change either absolute supremum. Because $\varphi_i(0)=0$, both nonabsolute suprema of the transformed sum and its negative are nonnegative. Their maximum, the absolute supremum, is at most their sum. Sign symmetry makes their expected values equal. Apply the nonabsolute contraction result to one of them, then bound its untransformed supremum by the absolute one. This proves (4). For countable classes apply it to increasing finite subsets containing zero and use monotone convergence; a determining countable subclass proves the separable case. ∎

Theorem (The square-root overlap scale for VC regret): Use the known-assignment setup above and let $\mathcal F=\{f_\pi:\pi\in\Pi\}$, where $|f_\pi|\le B$, $P f_\pi^2\le B$, and $B\ge1$. Put \[ H_n=\log(2G(n)),\qquad G(n)=\prod_{t=1}^T\sum_{k=0}^{\min(v_t,n)}\binom nk. \] Any measurable empirical maximizer to error $\xi\ge0$ satisfies \[ \mathbb E\{\sup_\pi V_P(\pi)-V_P(\widehat\pi)\} \le 4\sqrt{\frac{2B H_n}{n}}+ \frac{64B H_n}{n}+\xi. \tag{5} \] It also obeys the universal regret bound two. The guarantee is uniform in every observational law satisfying the displayed assumptions.
Proof: Condition on the observed sample. Its distinct policy-score vectors number at most $G(n)$ by the growth argument already proved. Let $S_n=\sup_f P_n f^2$. For a fixed score vector, the sign-sum exponential moment is bounded by $\exp(t^2\sum_i f(O_i)^2/2)$. Applying this to both signs of each of the at most $G(n)$ vectors and optimizing $t$ gives \[ \mathbb E_\sigma\sup_f\left|n^{-1}\sum_i\sigma_i f(O_i)\right| \le\sqrt{2H_n S_n/n}. \tag{6} \] If every vector is zero, both sides are zero and no optimization is needed. This is a conditional sign bound, not a population concentration claim for the data-dependent representatives.

Write $R=\mathcal R_n(\mathcal F)$. Jensen's inequality in (6) yields $R^2\le (2H_n/n)\mathbb E S_n$. Since $P f^2\le B$, \[ \mathbb E S_n\le B+ \mathbb E\sup_f|(P_n-P)f^2|. \tag{7} \] For completeness, the usual ghost-sample step gives, for any bounded class $\mathcal G$, \[ \mathbb E\sup_g|(P_n-P)g| \le\mathbb E\sup_g|(P_n-P_n')g| \le2\mathcal R_n(\mathcal G). \] The first inequality is conditional Jensen over an independent sample. For the second, swap each original/ghost pair with an independent sign; the joint distribution is unchanged. Triangle inequality for the two signed sums and their identical distributions gives the factor two. Apply this with $\mathcal G=\{f^2:f\in\mathcal F\}$. On $[-B,B]$ the square map vanishes at zero and is $2B$-Lipschitz. Lemma (4), conditional on the sample, therefore gives \[ \mathbb E\sup_f|(P_n-P)f^2| \le2\mathcal R_n(\mathcal F^2)\le8BR. \tag{8} \] Combining (6)--(8), \[ R^2\le\frac{2BH_n}{n}+\frac{16BH_n}{n}R. \] For nonnegative $R$, an inequality $R^2\le a+bR$ implies $R\le\sqrt a+b$, by its positive quadratic root or direct expansion. Thus $R\le\sqrt{2BH_n/n}+16BH_n/n$.

Finally for every $\pi$, empirical maximality yields \[ Pf_\pi-Pf_{\widehat\pi} \le |(P_n-P)f_\pi|+|(P_n-P)f_{\widehat\pi}|+\xi. \] Taking the supremum on the left is valid even if a population optimum is not attained. Take expectations and symmetrize the class $\mathcal F$ to obtain regret at most $4R+\xi$, proving (5). The original bounded outcomes imply the additional cap of two. ∎

For $B H_n/n\le1$, (5) is of order $\sqrt{B H_n/n}+\xi$, improving the leading overlap factor of (2). With $B=\varepsilon^{-T}$ the square-root horizon dependence agrees with the two-policy lower bound (3), up to the policy-complexity factor in this upper bound. It does not prove that $H_n$ is the sharp minimax complexity for every supplied policy class. In particular no matching lower dependence on every $v_t$ is claimed, and no high-probability concentration conclusion is inferred from an expected-regret theorem.

Version 3: a sharp complexity--horizon subexperiment

The preceding lower bound (3) has two policies. We now retain the rare complete treatment path but allow $v$ distinct decisions to be learned. This supplies a matching dependence on first-stage VC complexity in an explicit subexperiment, rather than only the horizon dependence. The binary-testing and hypercube method is classical; see [tsybakov] for background. The needed testing reduction is proved explicitly below.

Fix integers $v,T,n\ge1$ and $0<\varepsilon\le1/2$. Let a baseline context $C$ be uniform on $\{1,\ldots,v\}$ and observed before treatment. Additional continuous history variables, if included, are independent uniforms and do not enter the outcome law. Assignments are independent Bernoulli$(\varepsilon)$, independent of all histories. Let $q_T=\varepsilon^T$ and $H=\prod_{t=1}^T A_t$. For an unknown vector $\theta\in[-1/4,1/4]^v$, take \[ Y\mid C,A_1,\ldots,A_T\ \sim\ \operatorname{Ber}\left(\tfrac12+\theta_C H\right). \tag{6} \] This can be generated from an independent uniform outcome noise, so the same structural rule defines all intervention outcomes and sequential exchangeability holds. Histories may record preceding assignments explicitly; the rule does not condition an intervention on a future observational graph or treatment choice.

Let $\Pi_1$ be all maps from the observed context to $\{0,1\}$, and let every later $\Pi_t$ contain only the constant-one policy. There are $2^v$ product policies, the first-stage VC dimension is exactly $v$, and the later VC dimensions are zero. Indeed $v$ histories with distinct contexts are shattered, whereas a collection of $v+1$ histories contains a repeated context and cannot be shattered. Conditional on each discrete history stratum, the transition densities and outcome regressions are constant in the additional continuous coordinates, hence satisfy every fixed smoothness index with compatible radii. This is a declared finite-context experiment. No claim is made that the same construction, uniformly in $v$, belongs to every chosen Hölder ball on a single connected continuous support.

Theorem (Matching first-stage complexity and overlap dependence): For experiment (6) and its supplied policy class, there are universal constants $0<c<C<\infty$ such that \[ c\min\left\{1,\sqrt{\frac{v}{n\varepsilon^T}}\right\} \le\inf_{\widehat\pi\in\Pi}\sup_{\theta\in[-1/4,1/4]^v} \mathbb E_\theta\{V_\theta(\pi_\theta^*)- V_\theta(\widehat\pi)\} \le C\min\left\{1,\sqrt{\frac{v}{n\varepsilon^T}}\right\}. \tag{7} \] The infimum includes randomized measurable learners. The constants do not depend on $n,v,T$ or $\varepsilon$. Empirical importance-weighted value maximization attains the upper bound; in this subexperiment it can be implemented in $O(nT+v)$ arithmetic operations as shown below.
Proof: Under a policy $\pi$, every later action is one, so \[ V_\theta(\pi)=\tfrac12+ \frac1v\sum_{c=1}^v\theta_c\pi_1(c). \] The maximizing first-stage decision is $1$ for a positive coordinate and $0$ for a negative coordinate; either decision is optimal at zero. The known-assignment finite-class bound (1), with $M=2^v$ and $B=q_T^{-1}$, gives $C\{\sqrt{v/(nq_T)}+v/(nq_T)\}$ since $\log(2^{v+1})\le2v\log2$. For $v/(nq_T)\le1$, the second term is no larger than the first. Otherwise use the universal bounded regret bound. This proves the upper inequality.

For the lower inequality, set \[ \delta=\frac1{16}\min\left\{1,\sqrt{\frac{v}{nq_T}}\right\}, \qquad \theta_c=\delta s_c,\quad s\in\{-1,1\}^v. \] Let $P_s$ be one trajectory law. If $s$ and $s'$ differ only in coordinate $c$, the laws differ only when $C=c,H=1$, an event of probability $q_T/v$. On that event the outcome probabilities are $1/2+\delta$ and $1/2-\delta$ in one order or the other. For Bernoulli probabilities $a,b$, the inequality $\log u\le u-1$ gives \[ \operatorname{KL}(\operatorname{Ber}(a),\operatorname{Ber}(b)) \le\frac{(a-b)^2}{b(1-b)}. \] Here $\delta\le1/16<1/4$, so the right side is at most $32\delta^2$. Independence of trajectories therefore gives \[ \operatorname{KL}(P_s^n,P_{s'}^n) \le\frac{32nq_T\delta^2}{v}\le\frac18. \] Pinsker's inequality implies total variation at most $1/4$. For any learner, the event that its context-$c$ decision is one is a binary test between these adjacent laws. The two error probabilities sum to at least $1-\operatorname{TV}\ge3/4$. An independent random seed used by a randomized learner has the same law under both hypotheses and does not increase total variation, so the same inequality holds for randomized procedures.

Average over all $2^v$ sign vectors. For each fixed coordinate, group them into the $2^{v-1}$ pairs differing only at that coordinate. The average probability of a wrong context decision is at least $3/8$. For the law indexed by $s$, the exact regret is \[ \frac{\delta}{v}\sum_{c=1}^v \mathbf1\{\widehat\pi_1(c)\ne\mathbf1\{s_c=1\}\}. \] Summing the averaged error inequalities gives average expected regret at least $3\delta/8$. A supremum is at least this average, proving the lower bound in (7) with a universal positive constant. This explicitly proves the needed hypercube testing bound instead of assuming independent decisions by the learner across contexts. ∎

One may also optimize this particular empirical objective without enumerating $2^v$ policies: all complete-path contributions split by context, and for each context the learner selects the larger of the scores for first actions zero and one. This costs $O(nT+v)$ arithmetic operations after the supplied assignment probabilities are evaluated. For each context these are exactly its two additive terms in the full importance-weighted empirical value, including the observed outcomes on both first-action paths. Hence this coordinatewise maximization really attains the empirical optimum used in the upper-bound proof. Its existence does not give an optimizer for general history-dependent VC policy classes.

For any sequence of these experiments, including increasing $T$ and $v$, minimax regret tends to zero if and only if $v/(n\varepsilon^T)\longrightarrow0$, by (7). This is a uniform assertion for the specified sequence of finite-context classes, not a universal claim that $\sum_t v_t$ always determines all dynamic-policy minimax rates.

Interpretation and remaining task

The known-assignment VC upper bound from version 2 remains valid, and version 3 now proves matching complexity and horizon rates in a concrete finite-context product class, with an explicit efficient optimizer in that subexperiment. This identifies an information loss proportional to both the number of first-stage decisions and inverse complete-path overlap. It does not establish a sharp lower dependence on every stage's VC dimension, nor membership of the growing-context construction in an arbitrary fixed smoothness ball on a connected continuous support. Unknown assignment laws, the full transition/smoothness and dimension dependence, and optimization over general infinite policy classes remain unresolved parts of the catalogue target.

Completion Estimate

45%

This is a heuristic estimate for the full catalogue target. The new hypercube construction and matching learner settle the combined first-stage complexity and horizon scale in a nontrivial subclass. The full multi-stage complexity, continuous-support smoothness restrictions and unknown-nuisance minimax problem remain open in this attempt.

References

  • [agenda] C. Cinelli, A. Feller, G. Imbens, E. Kennedy, S. Magliacane and J. Zubizarreta. Challenges in Statistics: A Dozen Challenges in Causality and Causal Inference (2025), arXiv:2508.17099v1. https://arxiv.org/html/2508.17099v1.
  • [tsybakov] A. B. Tsybakov. Introduction to Nonparametric Estimation. Springer, 2009.

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Econ-67-A2 · Exploration · LLM-generated · gpt 6 astra ultra ·

GPT 6 Astra Ultra attempt on Econ-67, version 2

Sharp regret guarantees for dynamic treatment regimes: Variance-sensitive VC learning and a sharp horizon obstruction

Problem ID: C44-65. Status: Unresolved; rigorous restricted results.

Target and scope

For dynamic deterministic policies $\pi=(\pi_1,\ldots,\pi_T)$ with history-dependent decisions, the canonical target is minimax regret \[ \inf_{\widehat\pi\in\Pi}\sup_{P\in\mathcal P} \mathbb E_P\{\sup_{\pi\in\Pi}V_P(\pi)-V_P(\widehat\pi)\}. \] Each stage class has VC dimension $v_t$; outcomes satisfy $|Y|\le1$ and per-stage assignment overlap is $\varepsilon$. The dynamic-policy agenda appears in [agenda, Section 3.3.3]. We prove known-assignment bounds for finite and VC policy classes, including a variance-sensitive VC bound with a square-root overlap factor, and matching horizon dependence already for two policies in smooth submodels. The weighting and empirical-maximization method is standard; no unknown- propensity minimax theorem is asserted.

Uniform evaluation with known assignment laws

Let the supplied observational assignment probabilities be $q_t(a\mid H_t)\ge\varepsilon$. Set \[ R_\pi(O)=\prod_{t=1}^T\frac{\mathbf1\{A_t=\pi_t(H_t)\}} {q_t(A_t\mid H_t)},\qquad f_\pi=R_\pi Y. \] Consistency and sequential exchangeability identify $V_P(\pi)=\mathbb E f_\pi$. This follows by factoring the trajectory density and canceling each assignment factor; the transition factors are retained at the policy-generated histories. Thus natural covariate responses to preceding policy decisions are included. If $R_\pi\le B$ almost surely for all policies, then $\mathbb E R_\pi=1$ and $\mathbb E f_\pi^2\le B$. The universal choice from per-stage overlap is $B=\varepsilon^{-T}$.
Theorem: For a finite policy class of size $M$, let $\widehat\pi$ maximize $n^{-1}\sum_i f_\pi(O_i)$, or maximize it to deterministic error $\xi$. Then \[ \mathbb E\{\sup_\pi V_P(\pi)-V_P(\widehat\pi)\} \le C\left\{\sqrt{\frac{B\log(2M)}n} +\frac{B\log(2M)}n\right\}+\xi, \tag{1} \] with a universal constant $C$. Regret is also bounded by two.
Proof: For every fixed policy, $|f_\pi-\mathbb Ef_\pi|\le2B$ and $\operatorname{Var}(f_\pi)\le B$. The exponential-moment proof of Bernstein's inequality gives, for $z>0$, \[ P\left\{|(P_n-P)f_\pi|> \sqrt{2Bz/n}+4Bz/(3n)\right\}\le2e^{-z}. \] For completeness its moment bound uses $\mathbb E|Z|^k\le(2B)^{k-2}\mathbb E Z^2$ for centered $Z=f_\pi-\mathbb E f_\pi$, expands $\mathbb E e^{tZ}$, and sums the geometric upper bound on terms $k\ge2$; optimizing Markov's bound gives the displayed inequality. Union bound at $z=\log(2M)+s$ gives failure probability at most $e^{-s}$ for all policies simultaneously. Integrating this tail bounds the expected supremum by a constant times the two terms in (1). Empirical maximality implies regret at most $2\sup_\pi|(P_n-P)f_\pi|+\xi$, proving (1). ∎
Proposition: For the pointwise-separable product class in the canonical statement, let $G_t(n)=\sum_{k=0}^{\min(v_t,n)}\binom nk$ and $G(n)=\prod_t G_t(n)$. An approximate empirical maximizer satisfies \[ \mathbb E\{\sup_\pi V_P(\pi)-V_P(\widehat\pi)\} \le4B\sqrt{\frac{2\log(2G(n))}{n}}+\xi. \tag{2} \] For $1\le v_t\le n$, one may use $G_t(n)\le(en/v_t)^{v_t}$; $G_t(n)=1$ for $v_t=0$. This bound is generally weaker in $B$ than (1).
Proof: On any observed sample, each stage class has at most $G_t(n)$ binary label vectors by the VC growth bound. One elementary proof of that bound uses the recursion $G_v(n)\le G_v(n-1)+G_{v-1}(n-1)$, obtained by splitting labelings according to whether the last coordinate can be both labels; induction yields the binomial sum. The vectors of complete-path indicators therefore number at most $G(n)$. Weights $Y_i\prod_tq_t(A_{it}\mid H_{it})^{-1}$ are fixed on this sample and do not depend on the policy. Each corresponding score vector has Euclidean norm at most $B\sqrt n$. For independent Rademacher signs, the exponential inequality $\mathbb E_\sigma\exp(t\sum_i\sigma_i a_i) \le\exp(t^2\sum_i a_i^2/2)$ and a union bound give an expected absolute maximum at most $B\sqrt{2n\log(2G(n))}$. An independent ghost sample and Jensen's inequality symmetrize $\mathbb E\sup|(P_n-P)f|$ to at most twice that maximum divided by $n$. Pointwise separability makes the suprema measurable. The same empirical- maximization inequality as before proves (2). The standard binomial sum bound follows by expanding $(1+x)^n$ at $x=v_t/n$. ∎

Exponential information loss with one decision to learn

Theorem: For every $T\ge1$ and $0<\varepsilon<1/2$, there is a known-assignment model with $v_1=1$, $v_t=0$ for $t>1$, and smooth continuous-coordinate nuisances of every order, for which minimax regret is \[ \asymp\min\{1,(n\varepsilon^T)^{-1/2}\}. \tag{3} \] The constants can be chosen universal. The policy class has exactly two policies and includes all combinations of its specified stage classes.
Proof: Let assignments be independent Bernoulli$(\varepsilon)$ and histories, if continuous coordinates are required, include independent uniforms irrelevant to outcomes. One policy always assigns one; the other assigns zero at the first stage and one thereafter. Their stage classes are $\Pi_1=\{0,1\}$ and $\Pi_t=\{1\}$ for $t>1$. Let $Y\in\{-1,1\}$ have mean zero off the all-one assignment path and mean $\theta$ on that path. Choose two laws $\theta=\pm\Delta$. Sequential exchangeability holds by independent randomization, and conditional outcome means are constant within each discrete history stratum, satisfying any fixed Hölder index with an adequate radius. The policy values are respectively $\theta$ and zero. Choosing the wrong policy incurs regret $\Delta$. The laws differ only on a path with probability $q=\varepsilon^T$. For $\Delta\le1/2$, one-observation KL is at most $Cq\Delta^2$. Choose $\Delta=b\min\{1,(nq)^{-1/2}\}$ with $b$ sufficiently small. Pinsker's inequality bounds sample total variation by $1/2$; the sum of wrong-policy probabilities is at least $1/2$. Maximal regret is therefore at least $\Delta/4$. For the upper bound, (1) with $M=2$, $B=q^{-1}$ gives $C\{(nq)^{-1/2}+(nq)^{-1}\}$. When $nq\ge1$ the second term is smaller than the first; when $nq<1$ the universal regret bound two applies. This proves (3). ∎

A variance-sensitive bound for the infinite VC product class

The finite-class Bernstein bound (1) scales with $\sqrt B$ in its leading term, whereas the elementary VC bound (2) scales with $B$. The following argument closes that difference for expected regret, still with supplied assignment laws. It does not replace an empirical growth count by an unjustified union bound over random representative policies.

Let $\mathcal F$ be a pointwise separable class of measurable score functions. Define its expected Rademacher average, with an absolute supremum, by \[ \mathcal R_n(\mathcal F) =\mathbb E_{O,\sigma}\sup_{f\in\mathcal F} \left|\frac1n\sum_{i=1}^n\sigma_i f(O_i)\right|, \] where the signs are independent of the sample and of one another. Boundedness and separability allow reduction to a countable determining class, so the suprema and expectations below are measurable.

Lemma (Contraction with an explicit safe constant): For any finite set $A\subset\mathbb R^n$ and maps $\varphi_i$ with $\varphi_i(0)=0$ and Lipschitz constant at most $L$ on the needed coordinate range, \[ \mathbb E_\sigma\sup_{a\in A} \left|\sum_i\sigma_i\varphi_i(a_i)\right| \le2L\,\mathbb E_\sigma\sup_{a\in A} \left|\sum_i\sigma_i a_i\right|. \tag{4} \] The same bound holds for pointwise-separable bounded score vectors.
Proof: First consider a supremum without absolute values. Conditional on the other signs, let $U_a$ denote their contribution. For one coordinate, \[ \frac12\sup_a[U_a+\varphi_i(a_i)] +\frac12\sup_b[U_b-\varphi_i(b_i)] =\frac12\sup_{a,b}[U_a+U_b+\varphi_i(a_i)-\varphi_i(b_i)]. \] Lipschitz continuity bounds this by $\tfrac12\sup_{a,b}[U_a+U_b+L|a_i-b_i|]$. Since the pair $(a,b)$ ranges over all ordered pairs, exchanging it shows that the latter supremum equals $\sup_{a,b}[U_a+U_b+L(a_i-b_i)]$. Dividing by two and separating the suprema gives the sign average with $L a_i$ instead of $\varphi_i(a_i)$. Repeat for every coordinate. Thus nonabsolute Rademacher suprema contract with factor $L$.

Adjoin the zero vector to $A$; this does not change either absolute supremum. Because $\varphi_i(0)=0$, both nonabsolute suprema of the transformed sum and its negative are nonnegative. Their maximum, the absolute supremum, is at most their sum. Sign symmetry makes their expected values equal. Apply the nonabsolute contraction result to one of them, then bound its untransformed supremum by the absolute one. This proves (4). For countable classes apply it to increasing finite subsets containing zero and use monotone convergence; a determining countable subclass proves the separable case. ∎

Theorem (The square-root overlap scale for VC regret): Use the known-assignment setup above and let $\mathcal F=\{f_\pi:\pi\in\Pi\}$, where $|f_\pi|\le B$, $P f_\pi^2\le B$, and $B\ge1$. Put \[ H_n=\log(2G(n)),\qquad G(n)=\prod_{t=1}^T\sum_{k=0}^{\min(v_t,n)}\binom nk. \] Any measurable empirical maximizer to error $\xi\ge0$ satisfies \[ \mathbb E\{\sup_\pi V_P(\pi)-V_P(\widehat\pi)\} \le 4\sqrt{\frac{2B H_n}{n}}+ \frac{64B H_n}{n}+\xi. \tag{5} \] It also obeys the universal regret bound two. The guarantee is uniform in every observational law satisfying the displayed assumptions.
Proof: Condition on the observed sample. Its distinct policy-score vectors number at most $G(n)$ by the growth argument already proved. Let $S_n=\sup_f P_n f^2$. For a fixed score vector, the sign-sum exponential moment is bounded by $\exp(t^2\sum_i f(O_i)^2/2)$. Applying this to both signs of each of the at most $G(n)$ vectors and optimizing $t$ gives \[ \mathbb E_\sigma\sup_f\left|n^{-1}\sum_i\sigma_i f(O_i)\right| \le\sqrt{2H_n S_n/n}. \tag{6} \] If every vector is zero, both sides are zero and no optimization is needed. This is a conditional sign bound, not a population concentration claim for the data-dependent representatives.

Write $R=\mathcal R_n(\mathcal F)$. Jensen's inequality in (6) yields $R^2\le (2H_n/n)\mathbb E S_n$. Since $P f^2\le B$, \[ \mathbb E S_n\le B+ \mathbb E\sup_f|(P_n-P)f^2|. \tag{7} \] For completeness, the usual ghost-sample step gives, for any bounded class $\mathcal G$, \[ \mathbb E\sup_g|(P_n-P)g| \le\mathbb E\sup_g|(P_n-P_n')g| \le2\mathcal R_n(\mathcal G). \] The first inequality is conditional Jensen over an independent sample. For the second, swap each original/ghost pair with an independent sign; the joint distribution is unchanged. Triangle inequality for the two signed sums and their identical distributions gives the factor two. Apply this with $\mathcal G=\{f^2:f\in\mathcal F\}$. On $[-B,B]$ the square map vanishes at zero and is $2B$-Lipschitz. Lemma (4), conditional on the sample, therefore gives \[ \mathbb E\sup_f|(P_n-P)f^2| \le2\mathcal R_n(\mathcal F^2)\le8BR. \tag{8} \] Combining (6)--(8), \[ R^2\le\frac{2BH_n}{n}+\frac{16BH_n}{n}R. \] For nonnegative $R$, an inequality $R^2\le a+bR$ implies $R\le\sqrt a+b$, by its positive quadratic root or direct expansion. Thus $R\le\sqrt{2BH_n/n}+16BH_n/n$.

Finally for every $\pi$, empirical maximality yields \[ Pf_\pi-Pf_{\widehat\pi} \le |(P_n-P)f_\pi|+|(P_n-P)f_{\widehat\pi}|+\xi. \] Taking the supremum on the left is valid even if a population optimum is not attained. Take expectations and symmetrize the class $\mathcal F$ to obtain regret at most $4R+\xi$, proving (5). The original bounded outcomes imply the additional cap of two. ∎

For $B H_n/n\le1$, (5) is of order $\sqrt{B H_n/n}+\xi$, improving the leading overlap factor of (2). With $B=\varepsilon^{-T}$ the square-root horizon dependence agrees with the two-policy lower bound (3), up to the policy-complexity factor in this upper bound. It does not prove that $H_n$ is the sharp minimax complexity for every supplied policy class. In particular no matching lower dependence on every $v_t$ is claimed, and no high-probability concentration conclusion is inferred from an expected-regret theorem.

Interpretation and remaining task

Fixed VC complexity and smoothness do not prevent exponential horizon loss: the uncertain value is observed only on a complete treatment path. In the known-assignment finite-class experiment, a polynomial bound on the cumulative ratios $B_T$, together with polynomial $\log M_T$, is a sufficient condition for polynomial sample requirements by (1). For increasing horizons the exact subexperiment in (3) is consistent precisely when $n\varepsilon^{T_n}\to\infty$. These are statistical guarantees conditional on an optimization oracle; finite enumeration costs $M$ and can itself be exponential in $T$. The new argument supplies a stronger variance-sensitive VC guarantee for known assignments. Unknown assignments, joint transition/smoothness and history-dimension dependence, sharp lower policy-complexity bounds, and polynomial-time optimization over infinite policy classes remain unresolved parts of the original target.

Completion Estimate

40%

This is a heuristic estimate for the full catalogue target. The new VC argument closes the proved overlap-factor gap between finite and infinite policy classes, with a complete treatment of sample-dependent score vectors and expected regret. The full unknown-nuisance minimax rates, sharp policy-complexity lower bounds and computational guarantees remain unresolved.

References

  • [agenda] C. Cinelli, A. Feller, G. Imbens, E. Kennedy, S. Magliacane and J. Zubizarreta. Challenges in Statistics: A Dozen Challenges in Causality and Causal Inference (2025), arXiv:2508.17099v1. https://arxiv.org/html/2508.17099v1.

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Econ-67-A1 · Exploration · LLM-generated · gpt 6 astra ultra ·

GPT 6 Astra Ultra attempt on Econ-67

Sharp regret guarantees for dynamic treatment regimes: Known-assignment learning and a sharp two-policy horizon lower bound

Target and scope

For dynamic deterministic policies $\pi=(\pi_1,\ldots,\pi_T)$ with history-dependent decisions, the canonical target is minimax regret \[ \inf_{\widehat\pi\in\Pi}\sup_{P\in\mathcal P} \mathbb E_P\{\sup_{\pi\in\Pi}V_P(\pi)-V_P(\widehat\pi)\}. \] Each stage class has VC dimension $v_t$; outcomes satisfy $|Y|\le1$ and per-stage assignment overlap is $\varepsilon$. The dynamic-policy agenda appears in [agenda, Section 3.3.3]. We prove a known-assignment upper bound, a VC corollary, and a matching horizon dependence already for two policies in smooth submodels. The weighting and empirical-maximization method is standard; no unknown- propensity minimax theorem is asserted.

Uniform evaluation with known assignment laws

Let the supplied observational assignment probabilities be $q_t(a\mid H_t)\ge\varepsilon$. Set \[ R_\pi(O)=\prod_{t=1}^T\frac{\mathbf1\{A_t=\pi_t(H_t)\}} {q_t(A_t\mid H_t)},\qquad f_\pi=R_\pi Y. \] Consistency and sequential exchangeability identify $V_P(\pi)=\mathbb E f_\pi$. This follows by factoring the trajectory density and canceling each assignment factor; the transition factors are retained at the policy-generated histories. Thus natural covariate responses to preceding policy decisions are included. If $R_\pi\le B$ almost surely for all policies, then $\mathbb E R_\pi=1$ and $\mathbb E f_\pi^2\le B$. The universal choice from per-stage overlap is $B=\varepsilon^{-T}$.
Theorem: For a finite policy class of size $M$, let $\widehat\pi$ maximize $n^{-1}\sum_i f_\pi(O_i)$, or maximize it to deterministic error $\xi$. Then \[ \mathbb E\{\sup_\pi V_P(\pi)-V_P(\widehat\pi)\} \le C\left\{\sqrt{\frac{B\log(2M)}n} +\frac{B\log(2M)}n\right\}+\xi, \tag{1} \] with a universal constant $C$. Regret is also bounded by two.
Proof: For every fixed policy, $|f_\pi-\mathbb Ef_\pi|\le2B$ and $\operatorname{Var}(f_\pi)\le B$. The exponential-moment proof of Bernstein's inequality gives, for $z>0$, \[ P\left\{|(P_n-P)f_\pi|> \sqrt{2Bz/n}+4Bz/(3n)\right\}\le2e^{-z}. \] For completeness its moment bound uses $\mathbb E|Z|^k\le(2B)^{k-2}\mathbb E Z^2$ for centered $Z=f_\pi-\mathbb E f_\pi$, expands $\mathbb E e^{tZ}$, and sums the geometric upper bound on terms $k\ge2$; optimizing Markov's bound gives the displayed inequality. Union bound at $z=\log(2M)+s$ gives failure probability at most $e^{-s}$ for all policies simultaneously. Integrating this tail bounds the expected supremum by a constant times the two terms in (1). Empirical maximality implies regret at most $2\sup_\pi|(P_n-P)f_\pi|+\xi$, proving (1). ∎
Proposition: For the pointwise-separable product class in the canonical statement, let $G_t(n)=\sum_{k=0}^{\min(v_t,n)}\binom nk$ and $G(n)=\prod_t G_t(n)$. An approximate empirical maximizer satisfies \[ \mathbb E\{\sup_\pi V_P(\pi)-V_P(\widehat\pi)\} \le4B\sqrt{\frac{2\log(2G(n))}{n}}+\xi. \tag{2} \] For $1\le v_t\le n$, one may use $G_t(n)\le(en/v_t)^{v_t}$; $G_t(n)=1$ for $v_t=0$. This bound is generally weaker in $B$ than (1).
Proof: On any observed sample, each stage class has at most $G_t(n)$ binary label vectors by the VC growth bound. One elementary proof of that bound uses the recursion $G_v(n)\le G_v(n-1)+G_{v-1}(n-1)$, obtained by splitting labelings according to whether the last coordinate can be both labels; induction yields the binomial sum. The vectors of complete-path indicators therefore number at most $G(n)$. Weights $Y_i\prod_tq_t(A_{it}\mid H_{it})^{-1}$ are fixed on this sample and do not depend on the policy. Each corresponding score vector has Euclidean norm at most $B\sqrt n$. For independent Rademacher signs, the exponential inequality $\mathbb E_\sigma\exp(t\sum_i\sigma_i a_i) \le\exp(t^2\sum_i a_i^2/2)$ and a union bound give an expected absolute maximum at most $B\sqrt{2n\log(2G(n))}$. An independent ghost sample and Jensen's inequality symmetrize $\mathbb E\sup|(P_n-P)f|$ to at most twice that maximum divided by $n$. Pointwise separability makes the suprema measurable. The same empirical- maximization inequality as before proves (2). The standard binomial sum bound follows by expanding $(1+x)^n$ at $x=v_t/n$. ∎

Exponential information loss with one decision to learn

Theorem: For every $T\ge1$ and $0<\varepsilon<1/2$, there is a known-assignment model with $v_1=1$, $v_t=0$ for $t>1$, and smooth continuous-coordinate nuisances of every order, for which minimax regret is \[ \asymp\min\{1,(n\varepsilon^T)^{-1/2}\}. \tag{3} \] The constants can be chosen universal. The policy class has exactly two policies and includes all combinations of its specified stage classes.
Proof: Let assignments be independent Bernoulli$(\varepsilon)$ and histories, if continuous coordinates are required, include independent uniforms irrelevant to outcomes. One policy always assigns one; the other assigns zero at the first stage and one thereafter. Their stage classes are $\Pi_1=\{0,1\}$ and $\Pi_t=\{1\}$ for $t>1$. Let $Y\in\{-1,1\}$ have mean zero off the all-one assignment path and mean $\theta$ on that path. Choose two laws $\theta=\pm\Delta$. Sequential exchangeability holds by independent randomization, and conditional outcome means are constant within each discrete history stratum, satisfying any fixed Hölder index with an adequate radius. The policy values are respectively $\theta$ and zero. Choosing the wrong policy incurs regret $\Delta$. The laws differ only on a path with probability $q=\varepsilon^T$. For $\Delta\le1/2$, one-observation KL is at most $Cq\Delta^2$. Choose $\Delta=b\min\{1,(nq)^{-1/2}\}$ with $b$ sufficiently small. Pinsker's inequality bounds sample total variation by $1/2$; the sum of wrong-policy probabilities is at least $1/2$. Maximal regret is therefore at least $\Delta/4$. For the upper bound, (1) with $M=2$, $B=q^{-1}$ gives $C\{(nq)^{-1/2}+(nq)^{-1}\}$. When $nq\ge1$ the second term is smaller than the first; when $nq<1$ the universal regret bound two applies. This proves (3). ∎

Interpretation and remaining task

Fixed VC complexity and smoothness do not prevent exponential horizon loss: the uncertain value is observed only on a complete treatment path. In the known-assignment finite-class experiment, a polynomial bound on the cumulative ratios $B_T$, together with polynomial $\log M_T$, is a sufficient condition for polynomial sample requirements by (1). For increasing horizons the exact subexperiment in (3) is consistent precisely when $n\varepsilon^{T_n}\to\infty$. These are statistical guarantees conditional on an optimization oracle; finite enumeration costs $M$ and can itself be exponential in $T$. Unknown assignments, transition smoothness, history dimensions, stronger variance-sensitive VC bounds, and polynomial-time optimization over infinite policy classes are the unresolved parts of the original target.

References

  • [agenda] C. Cinelli, A. Feller, G. Imbens, E. Kennedy, S. Magliacane and J. Zubizarreta. Challenges in Statistics: A Dozen Challenges in Causality and Causal Inference (2025), arXiv:2508.17099v1. https://arxiv.org/html/2508.17099v1.

Completion Estimate

30%

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