Conformal counterfactual inference enables network operators to use logged telemetry to reliably answer 'what-if' questions about network operation. These answers typically take the form of prediction sets that contain, with a user-defined probability, the key performance indicators (KPIs) that would have been observed under alternative control actions. A key challenge is that logged telemetry may omit variables used by the controller, resulting in hidden confounding and invalidating the statistical guarantees of counterfactual analysis. In principle, this issue can be addressed using randomized telemetry, collected by assigning control actions independently of the network state. However, because such randomization may disrupt normal operation, randomized telemetry is typically scarce, causing counterfactual analysis based solely on it to produce uninformative prediction sets. To address these challenges, we propose Confounding-Valid Counterfactual Conformal Inference (CV-CCI), which combines abundant, potentially confounded observational telemetry with limited randomized data through the General Synthetic-Powered Inference (GESPI) principle. CV-CCI leverages observational data to improve efficiency while using randomized data to retain finite-sample coverage guarantees under arbitrary hidden confounding. Experiments on two representative radio access network (RAN) control tasks show that CV-CCI remains valid under hidden confounding while producing more efficient prediction sets than state-of-the-art confounding-valid baselines.
Figures & tables
Fig. 1: A RAN controller selects between proportional-fair channel-aware (PFCA) and round-robin (RR) scheduling based on the UEs’ backlogs and available radio resources. The network operation is logged via telemetry that records the backlogs, selected scheduler, and residual backlog after scheduling, but omits resource availability. Counterfactual analysis can use abundant observational telemetry collected during normal operation and limited randomized telemetry in which the scheduler is selected independently of the network context. The goal is to construct a prediction set for the residual backlog that would have been observed under the alternative scheduler. Prediction intervals calibrated exclusively on observational telemetry (red) may fail to cover the ground-truth residual backlog (green), whereas those calibrated exclusively on randomized telemetry (blue) are valid but wide. CV-CCI combines both data sources to produce valid and more informative prediction sets (red-and-blue dashed).
Fig. 2: Illustration of the considered counterfactual KPI estimation setting. The controller observes the full network context X=(X~,U) , selects an action A under either the observational or randomized regime S , and records the resulting KPI Y . Telemetry contains (X~,A,Y,S) but not the hidden context U . Using the recorded telemetry, the goal is to construct a prediction set for the potential KPI Y(a) associated with a target action a and a logged state X~=x~ .
Fig. 3: Causal structure under hidden confounding. In the observational regime, action A is selected using the full context X=(X~,U) , while the unobserved component U may also affect the potential KPIs Y(a)a∈A . Conditioning only on the logged state X~ therefore does not, in general, block the dependence between A and the potential outcomes. Randomized assignment removes this dependence by making A independent of the context.
Fig. 4: At each scheduling frame, the MAC scheduler selects an action A∈{RR,PFCA} based on the full context X=(X~,U) , where X~ contains the logged UE backlogs, CQIs, and packet delays, and U represents the instantaneous PRB budget assigned to the slice by a slicing application. After the selected scheduling policy is applied, the network produces the residual-backlog KPI Y . The PRB budget is available to the scheduler, but is not recorded in the telemetry used for counterfactual inference. Because it may affect both the action selection and the resulting KPI, it acts as a hidden confounder.
Fig. 5: Empirical coverage (left) and prediction-set efficiency (right) for the RR potential outcome as a function of the confounding strength λ . The dashed line indicates the nominal coverage level 1−α=0.90 , while the dotted line indicates the guardrail level 1−α−ϵ=0.875 . Boxplots summarize the results over nfolds=100 folds.
Confounding ( λ )
CV-CCI
Guardrail
0.00
30%
47%
0.25
23%
38%
0.50
24%
41%
0.75
28%
48%
1.00
26%
41%
TABLE I: Empirical failure rates of CV-CCI and Guardrail for the PFCA potential outcome.
Fig. 6: Empirical coverage (left) and efficiency (right) of CV-CCI and Guardrail for the PFCA potential outcome Y(PFCA) as a function of the miscoverage tolerance ϵ .
Fig. 7: A serving base station (BS1) proposes a handover using the logged RSS histories X~=[RSS1−τ,RSS2−τ] , while a neighboring base station (BS2) accepts or rejects the request based on its instantaneous load U=ℓ2 . The resulting action A∈{0,1} determines whether the UE remains connected to BS1 or is handed over to BS2, and the network produces the corresponding throughput KPI Y . Telemetry records (X~,A,Y) but not the target-cell load U . Because U may influence both the handover decision and the resulting throughput, it acts as a hidden confounder in counterfactual KPI inference.
Fig. 8: Empirical coverage (left) and prediction-interval efficiency (right) for the handover treatment outcome ( Y(1) ) as a function of the confounding strength λ . The dashed line indicates the nominal coverage level 1−α=0.90 , while the dotted line indicates the guardrail level 1−α−ϵ=0.875 . Boxplots summarize the results over nfolds=100 folds.
Confounding ( λ )
CV-CCI
Guardrail
0
9%
18%
2
7%
28%
4
12%
25%
6
10%
18%
8
13%
40%
TABLE II: Empirical failure rates of CV-CCI and Guardrail for the no-handover potential outcome.
Fig. 9: Empirical coverage (left) and efficiency (right) of CV-CCI and Guardrail for the no-handover potential outcome Y(0) as a function of the miscoverage tolerance ϵ .
Conformal counterfactual prediction constructs prediction sets with finite-sample coverage guarantees for counterfactual outcomes and individual treatment effects under the no-interference assumption. In this work, we relax this assumption by allowing each unit's potential outcomes to depend on other units' treatments and covariates. In this setting, propensity-score reweighting does not restore weighted exchangeability, and existing methods may fail to achieve valid coverage. To address this issue, we develop interference-adjusted weighted conformal prediction that accounts for interference by constructing an observable upper bound on the ideal and unobserved conformal p-value under the target intervention. The resulting prediction sets provide finite-sample marginal coverage guarantees for counterfactual outcomes and individual treatment effects in both transductive and inductive settings. We also derive a sharper construction when intervention-induced changes in nonconformity scores are bounded. Numerical experiments show that our methods preserve nominal coverage, whereas existing methods may not.
Matteo Zecchin, Osvaldo Simeone
Communication Systems Department, EURECOM, Sophia Antipolis, France · Institute for Intelligent Networked Systems (INSI), Northeastern University London, London, UK
Online conformal prediction can fail when predictions shape actions and actions determine which outcomes enter calibration. Standard adaptive methods may retain marginal coverage while systematically miscovering the counterfactual outcomes of rarely selected actions. This paper formalizes the failure through counterfactual coverage and introduces Propensity-Weighted Online Conformal Prediction, an inverse-propensity-weighted recursion that debiases calibration. A doubly robust variant further reduces nuisance bias to the product of outcome-model and propensity errors. Under positivity, the resulting coverage rate matches an information-theoretic lower bound up to logarithmic factors. Experiments on synthetic decision tasks, open bandit data, and financial rebalancing show that PW-OCP and DR-OCP improve counterfactual coverage and downstream regret without sacrificing prediction-set sharpness.
Xinyu Qiao, Yichen Lin, Kaihong Ji +2
Shanghai Jiao Tong University, Shanghai, China · Alibaba Group
This article considers an online version of conformal inference, called adaptive conformal inference [ACI] and introduced by Gibbs and Candès (2021): prediction sets are issued sequentially, after observing features and before the outcomes are revealed. These sets are evaluated both in terms of validity (the fraction of rounds where the outcome was lying in the prediction set) and efficiency (the average lengths of the prediction sets). The two criteria point to different directions (validity favors larger sets). We also target a wide range of scenarios, with exchangeable data and arbitrary data (lack of any stochastic guarantees) as two extremes. A series of existing strategies for ACI typically guarantee that empirical coverage converges to the desired level for arbitrary sequences, but they generally lack simultaneous efficiency guarantees. To provide a unified study, we first formulate ACI as a repeated two-player game with finite action sets and vector-valued payoffs encoding validity and efficiency. Building on this reformulation, we introduce a strategy based on Blackwell approachability and on its opportunistic extension by Bernstein et al. (2014) that ensures validity while adapting the efficiency of the prediction intervals to the underlying degree of stochasticity of the opponent player. The resulting guarantee is "best of many worlds": it recovers the relevant efficiency guarantees in exchangeable and adversarial settings, and provides guarantees in intermediate settings that arise in typical applications such as the forecasting of time series.
Guillaume Principato, Gilles Stoltz
Universit´e Paris-Saclay, CNRS, Inria, Laboratoire de math´ematiques d’Orsay, 91405, Orsay, France · EDF R&D, Palaiseau, France · HEC Paris, Jouy-en-Josas, France