arXiv subjects

François Petit

Publications and source records attributed to François Petit.

8 recordsLinked to original sources

arXiv2026

Formalization and consistency of clone-censor-weight

Target trial emulation has become a standard framework for causal inference from observational data. Within this paradigm, the clone-censor-weight (CCW) methodology provides a practical way to deal with complex treatment and adherence definitions when treatment regimes are not distinguishable at baseline. Despite its increasing use, the statistical interpretation of CCW remains limited. In this work, we formalize the CCW methodology from a statistical viewpoint based on admissible sets and stochastic interventions. We characterize the causal estimands targeted by CCW, establish consistency of CCW estimators under standard identification assumptions, and provide theoretical guarantees for bootstrap-based inference methods.

stat.ME↗

arXiv2026

crossfit: A Graph-Based Cross-Fitting Engine in R

Cross-fitting is a key ingredient in many semiparametric estimation procedures, such as double/debiased machine learning (DML), enabling valid estimation of low-dimensional targets in the presence of high-dimensional nuisance functions by enforcing out-of-sample use of nuisance predictions. crossfit is an R package that provides a general-purpose, estimator-agnostic cross-fitting engine. Users specify (i) a target functional and (ii) a directed acyclic graph (DAG) of nuisance models, with node-specific training fold widths and target-specific evaluation windows. The engine executes a reproducible schedule over folds, panels, and repetitions, returning either a scalar estimate (mode="estimate") or a cross-fitted predictor function for application to new data (mode="predict"). Beyond standard cross-fitting, crossfit implements fold-allocation modes that control how training data are shared across nuisance components, including disjoint and independence-enforcing allocations that duplicate reused nodes to reduce dependence between nuisance branches. The implementation targets simulation-heavy benchmarking and method development, with explicit and auditable schedules, defensive validation of specifications and nuisance dependencies, reuse-aware caching to avoid redundant refits, and failure isolation policies for large experiment grids. The crossfit package is available on CRAN, openly developed on GitHub under GPL-3, and is intended as a lightweight, tested foundation to prototype and empirically evaluate cross-fitted estimators with explicit control over fold geometry, dependence, and computation.

stat.CO↗

arXiv2025

Clustering methods for Categorical Time Series and Sequences : A scoping review

Objective: To provide an overview of clustering methods for categorical time series (CTS), a data structure commonly found in epidemiology, sociology, biology, and marketing, and to support method selection in regards to data characteristics. Methods: We searched PubMed, Web of Science, and Google Scholar, from inception up to November 2024 to identify articles that propose and evaluate clustering techniques for CTS. Methods were classified according to three major families -- distance-based, feature-based, and model-based -- and assessed on their ability to handle data challenges such as variable sequence length, multivariate data, continuous time, missing data, time-invariant covariates, and large data volumes. Results: Out of 14607 studies, we included 124 articles describing 129 methods, spanning domains such as artificial intelligence, social sciences, and epidemiology. Distance-based methods, particularly those using Optimal Matching, were most prevalent, with 56 methods. We identified 28 model-based methods, which demonstrated superior flexibility for handling complex data structures such as multivariate data, continuous time and time-invariant covariates. We also recorded 45 feature-based approaches, which were on average more scalable but less flexible. A searchable Web application was developed to facilitate method selection based on dataset characteristics ( https://cts-clustering-scoping-review-7sxqj3sameqvmwkvnzfynz.streamlit.app/ ) Discussion: While distance-based methods dominate, model-based approaches offer the richest modeling potential but are less scalable. Feature-based methods favor performance over flexibility, with limited support for complex data structures. Conclusion: This review highlights methodological diversity and gaps in CTS clustering. The proposed typology aims to guide researchers in selecting methods for their specific use cases.

stat.ME↗

arXiv2025

Evaluating virtual-control-augmented trials for reproducing treatment effect from original RCTs

This study investigates the use of virtual patient data to augment control arms in randomised controlled trials (RCTs). Using data from the IST and IST3 trials, we simulated RCTs in which the recruitment in the control arms would stop after a fraction of the initially planned sample size, and would be completed by virtual patients generated by CTGAN and TVAE, two AI algorithms trained on the recruited control patients. In IST, the absolute risk difference(ARD) on death or dependency at 14 days was -0.012 (SE 0.014). Completing the control arm by CTGAN-generated virtual patients after the recruitment of 10% and 50% of participants, yielded an ARD of 0.004 (SE 0.014) (relative difference 133%) and -0.021 (SE 0.014) (relative difference 76%), respectively. Results were comparable with IST3 or TVAE. This is the first empirical demonstration of the risk of errors and misleading conclusions associated with generating virtual controls solely from trial data.

stat.ME↗

arXiv2025

Estimating Complier Average Causal Effects with Mixtures of Experts

Treatment non-compliance, where individuals deviate from their assigned experimental conditions, frequently complicates the estimation of causal effects. To address this, we introduce a novel learning framework based on a mixture of experts architecture to estimate the Complier Average Causal Effect (CACE). Our framework provides a flexible alternative to classical instrumental variable methods by relaxing their strict monotonicity and exclusion restriction assumptions. We develop a principled, two-step procedure where each step is optimized with a dedicated Expectation-Maximization (EM) algorithm. Crucially, we provide formal proofs that the model's components are identifiable, ensuring the learning procedure is well-posed. The resulting CACE estimators are proven to be consistent and asymptotically normal. Extensive simulations demonstrate that our method achieves a substantially lower root mean squared error than traditional instrumental variable approaches when their assumptions fail, an advantage that persists even when our own mixture of experts are misspecified. We illustrate the framework's practical utility on data from a large-scale randomized trial.

stat.ME↗

arXiv2025

Optimal treatment regimes for the net benefit of a treatment

We develop a mathematical framework to define an optimal individualized treatment rule (ITR) within the context of prioritized outcomes in a randomized controlled trial. Our optimality criterion is based on the framework of generalized pairwise comparisons. We propose two approaches for estimating optimal ITRs on a pairwise basis. The first approach is a variant of the k-nearest neighbors algorithm. The second approach is a meta-learning method based on a randomized bagging scheme, which enables the use of any classification algorithm to construct an ITR. We investigate the theoretical properties of these estimation procedures, evaluate their performance through Monte Carlo simulations, and demonstrate their application to clinical trial data.

stat.ME↗

arXiv2024

Do machine learning methods lead to similar individualized treatment rules? A comparison study on real data

Identifying patients who benefit from a treatment is a key aspect of personalized medicine, which allows the development of individualized treatment rules (ITRs). Many machine learning methods have been proposed to create such rules. However, to what extent the methods lead to similar ITRs, i.e., recommending the same treatment for the same individuals is unclear. In this work, we compared 22 of the most common approaches in two randomized control trials. Two classes of methods can be distinguished. The first class of methods relies on predicting individualized treatment effects from which an ITR is derived by recommending the treatment evaluated to the individuals with a predicted benefit. In the second class, methods directly estimate the ITR without estimating individualized treatment effects. For each trial, the performance of ITRs was assessed by various metrics, and the pairwise agreement between all ITRs was also calculated. Results showed that the ITRs obtained via the different methods generally had considerable disagreements regarding the patients to be treated. A better concordance was found among akin methods. Overall, when evaluating the performance of ITRs in a validation sample, all methods produced ITRs with limited performance, suggesting a high potential for optimism. For non-parametric methods, this optimism was likely due to overfitting. The different methods do not lead to similar ITRs and are therefore not interchangeable. The choice of the method strongly influences for which patients a certain treatment is recommended, drawing some concerns about their practical use.

stat.AP↗

arXiv2021

Wasserstein Random Forests and Applications in Heterogeneous Treatment Effects

We present new insights into causal inference in the context of Heterogeneous Treatment Effects by proposing natural variants of Random Forests to estimate the key conditional distributions. To achieve this, we recast Breiman's original splitting criterion in terms of Wasserstein distances between empirical measures. This reformulation indicates that Random Forests are well adapted to estimate conditional distributions and provides a natural extension of the algorithm to multivariate outputs. Following the philosophy of Breiman's construction, we propose some variants of the splitting rule that are well-suited to the conditional distribution estimation problem. Some preliminary theoretical connections are established along with various numerical experiments, which show how our approach may help to conduct more transparent causal inference in complex situations.

stat.ML↗