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arxiv: 2606.30500 · v1 · pith:7QSCOUGX · submitted 2026-06-29 · stat.ML · cs.LG· stat.CO

Doubly Robust Adaptive Conformal Inference for Causal Effects Under Temporal Dependence

Andreas Koukorinis , Ricardo Silva This is my paper

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel 2026-06-30 03:33 UTCgrok-4.3pith:7QSCOUGXrecord.jsonopen to challenge →

classification stat.ML cs.LGstat.CO
keywords causal inferenceconformal predictiondoubly robust estimationtemporal dependenceprediction intervalsadaptive inference
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The pith

DR-ACI constructs prediction intervals for doubly robust pseudo-outcomes under temporal dependence.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes doubly robust adaptive conformal inference, or DR-ACI. This procedure builds prediction intervals for doubly robust pseudo-outcomes. It targets settings where observations show temporal dependence. A reader would care if the method delivers coverage guarantees that standard conformal approaches lose under serial correlation in causal data.

Core claim

DR-ACI constructs prediction intervals for doubly robust pseudo-outcomes under temporal dependence by applying an adaptive conformal procedure to these pseudo-outcomes.

What carries the argument

Doubly robust adaptive conformal inference (DR-ACI), which combines doubly robust pseudo-outcomes with adaptive conformal inference to produce intervals with coverage guarantees under temporal dependence.

Load-bearing premise

The temporal dependence structure permits valid coverage guarantees for the adaptive conformal procedure applied to the doubly robust pseudo-outcomes.

What would settle it

A simulation study or real dataset where the empirical coverage of DR-ACI intervals falls below the nominal level under a temporal dependence pattern not covered by the stated guarantees.

Figures

Figures reproduced from arXiv: 2606.30500 by Andreas Koukorinis, Ricardo Silva.

Figure 1
Figure 1. Figure 1: Temporal block cross-fitting with guard bands. When calibrating on block [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Coverage of the DR pseudo-outcome ψ DR t (not the latent CATE) and interval width as functions of mixing strength ρ. Left: All methods maintain valid pseudo-outcome coverage across mixing levels; VS-DR-ACI achieves the best calibration (closest to nominal). Right: VS-DR-ACI maintains the narrowest intervals while DR-ACI intervals widen under strong dependence. 5.5. Stress Test 1: Mixing Gap (Lemma 9) The m… view at source ↗
Figure 3
Figure 3. Figure 3: Empirical coverage and interval width as temporal dependence strengthens. The [PITH_FULL_IMAGE:figures/full_fig_p016_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Intraday volatility pattern in RX1 futures (April–October 2018). The U-shape [PITH_FULL_IMAGE:figures/full_fig_p024_4.png] view at source ↗
read the original abstract

We propose doubly robust adaptive conformal inference (DR-ACI), which constructs prediction intervals for doubly robust pseudo-outcomes under temporal dependence.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 1 minor

Summary. The paper proposes doubly robust adaptive conformal inference (DR-ACI), which constructs prediction intervals for doubly robust pseudo-outcomes under temporal dependence.

Significance. If valid coverage guarantees can be established for DR pseudo-outcomes under temporal dependence, the method would extend conformal inference techniques to causal settings with dependent data, potentially improving robustness in time-series causal inference.

minor comments (1)
  1. The provided manuscript text consists only of the abstract; without access to the full derivations, assumptions, theorems, or experiments, a complete technical assessment is not possible.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their summary of our manuscript proposing doubly robust adaptive conformal inference (DR-ACI) for prediction intervals on doubly robust pseudo-outcomes under temporal dependence. The significance assessment correctly identifies the potential extension of conformal methods to causal settings with dependent data. The recommendation is listed as uncertain, but the report contains no specific major comments to address. We remain available to respond to any additional points or concerns.

Circularity Check

0 steps flagged

No circularity detected; derivation chain not visible in abstract

full rationale

The abstract proposes DR-ACI without equations, fitted parameters, or self-citations that reduce any claimed result to its own inputs by construction. No load-bearing steps of the enumerated kinds are present or quotable. The paper is treated as self-contained at the level of the provided text, consistent with the default expectation that most papers exhibit no circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available, which does not identify or describe any free parameters, axioms, or invented entities.

pith-pipeline@v0.9.1-grok · 5530 in / 956 out tokens · 45610 ms · 2026-06-30T03:33:24.269522+00:00 · methodology

discussion (0)

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Reference graph

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