Causal inference for the expected number of recurrent events in the presence of a terminal event

• URL: http://arxiv.org/abs/2306.16571v3
• Date: Sun, 28 Sep 2025 13:40:44 GMT
• Title: Causal inference for the expected number of recurrent events in the presence of a terminal event
• Authors: Benjamin R. Baer, Trang Bui, Daniel Mork, Robert L. Strawderman, Ashkan Ertefaie,
• Abstract summary: We develop a multiply robust estimation framework for causal inference in recurrent event data with a terminal failure event.<n>We show that the estimand can be identified under a weaker condition than conditionally independent censoring.
• Score: 0.2446672595462589
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While recurrent event analyses have been extensively studied, limited attention has been given to causal inference within the framework of recurrent event analysis. We develop a multiply robust estimation framework for causal inference in recurrent event data with a terminal failure event. We define our estimand as the vector comprising both the expected number of recurrent events and the failure survival function evaluated along a sequence of landmark times. We show that the estimand can be identified under a weaker condition than conditionally independent censoring and derive the associated class of influence functions under general censoring and failure distributions (i.e., without assuming absolute continuity). We propose a particular estimator within this class for further study, conduct comprehensive simulation studies to evaluate the small-sample performance of our estimator, and illustrate the proposed estimator using a large Medicare dataset to assess the causal effect of PM$_{2.5}$ on recurrent cardiovascular hospitalization.

Related papers

• Learning Survival Models with Right-Censored Reporting Delays [10.312968200748118]
  Survival analysis is a statistical technique used to estimate the time until an event occurs.<n> adjusting for reporting delays under practical constraints remains a significant challenge in the insurance industry.<n>Our study addresses this challenge by jointly modeling the parametric hazard functions of event occurrences and report timings.
  arXiv  Detail & Related papers  (2025-10-06T01:16:57Z)
• Debiased Ill-Posed Regression [8.495265117285223]
  We propose a debiased estimation strategy based on the influence function of a modification of the projected error.<n>Our proposed estimator possesses a second-order bias with respect to the involved nuisance functions.
  arXiv  Detail & Related papers  (2025-05-27T06:47:33Z)
• Behavior of prediction performance metrics with rare events [0.8773694701994543]
  Area under the receiving operator characteristic curve (AUC) is commonly reported alongside prediction models for binary outcomes.<n>Recent articles have raised concerns that AUC might be a misleading measure of prediction performance in the rare event setting.<n>We conducted a simulation study to determine when or whether AUC is unstable in the rare event setting.
  arXiv  Detail & Related papers  (2025-04-22T18:20:11Z)
• Robust Probabilistic Model Checking with Continuous Reward Domains [1.7829696876801548]
  We propose a novel method for handling both continuous and discrete reward distributions in Discrete Time Markov Chains. Our method approximates the reward distribution with theoretically bounded error while preserving the statistical properties of the true distribution.
  arXiv  Detail & Related papers  (2025-02-06T22:03:18Z)
• Interval Estimation of Coefficients in Penalized Regression Models of Insurance Data [3.5637073151604093]
  Tweedie exponential dispersion family is a popular choice among many to model insurance losses. It is often important to obtain credibility (inference) of the most important features that describe the endogenous variables.
  arXiv  Detail & Related papers  (2024-10-01T18:57:18Z)
• Risk and cross validation in ridge regression with correlated samples [72.59731158970894]
  We provide training examples for the in- and out-of-sample risks of ridge regression when the data points have arbitrary correlations. We further extend our analysis to the case where the test point has non-trivial correlations with the training set, setting often encountered in time series forecasting. We validate our theory across a variety of high dimensional data.
  arXiv  Detail & Related papers  (2024-08-08T17:27:29Z)
• Deep State-Space Generative Model For Correlated Time-to-Event Predictions [54.3637600983898]
  We propose a deep latent state-space generative model to capture the interactions among different types of correlated clinical events. Our method also uncovers meaningful insights about the latent correlations among mortality and different types of organ failures.
  arXiv  Detail & Related papers  (2024-07-28T02:42:36Z)
• Score Matching-based Pseudolikelihood Estimation of Neural Marked Spatio-Temporal Point Process with Uncertainty Quantification [59.81904428056924]
  We introduce SMASH: a Score MAtching estimator for learning markedPs with uncertainty quantification. Specifically, our framework adopts a normalization-free objective by estimating the pseudolikelihood of markedPs through score-matching. The superior performance of our proposed framework is demonstrated through extensive experiments in both event prediction and uncertainty quantification.
  arXiv  Detail & Related papers  (2023-10-25T02:37:51Z)
• Doubly Robust Proximal Causal Learning for Continuous Treatments [56.05592840537398]
  We propose a kernel-based doubly robust causal learning estimator for continuous treatments. We show that its oracle form is a consistent approximation of the influence function. We then provide a comprehensive convergence analysis in terms of the mean square error.
  arXiv  Detail & Related papers  (2023-09-22T12:18:53Z)
• CenTime: Event-Conditional Modelling of Censoring in Survival Analysis [49.44664144472712]
  We introduce CenTime, a novel approach to survival analysis that directly estimates the time to event. Our method features an innovative event-conditional censoring mechanism that performs robustly even when uncensored data is scarce. Our results indicate that CenTime offers state-of-the-art performance in predicting time-to-death while maintaining comparable ranking performance.
  arXiv  Detail & Related papers  (2023-09-07T17:07:33Z)
• Maximum Likelihood With a Time Varying Parameter [0.0]
  We consider the problem of tracking an unknown time varying parameter that characterizes the evolution of a sequence of independent observations. We propose a gradient descent-based scheme in which the log-likelihood of the observations acts as time varying gain function.
  arXiv  Detail & Related papers  (2023-02-28T12:40:42Z)
• Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment [72.50906475214457]
  The goal of sequential event prediction is to estimate the next event based on a sequence of historical events. In practice, the next-event prediction models are trained with sequential data collected at one time. We propose a framework with hierarchical branching structures for learning context-specific representations.
  arXiv  Detail & Related papers  (2022-10-24T07:54:13Z)
• Data-Driven Influence Functions for Optimization-Based Causal Inference [105.5385525290466]
  We study a constructive algorithm that approximates Gateaux derivatives for statistical functionals by finite differencing. We study the case where probability distributions are not known a priori but need to be estimated from data.
  arXiv  Detail & Related papers  (2022-08-29T16:16:22Z)
• Inference on Strongly Identified Functionals of Weakly Identified Functions [71.42652863687117]
  We study a novel condition for the functional to be strongly identified even when the nuisance function is not. We propose penalized minimax estimators for both the primary and debiasing nuisance functions.
  arXiv  Detail & Related papers  (2022-08-17T13:38:31Z)
• Data Augmentation in the Underparameterized and Overparameterized Regimes [7.326504492614808]
  We quantify how data augmentation affects the variance and limiting distribution of estimates. The results confirm some observations made in machine learning practice, but also lead to unexpected findings.
  arXiv  Detail & Related papers  (2022-02-18T11:32:41Z)
• Enabling Counterfactual Survival Analysis with Balanced Representations [64.17342727357618]
  Survival data are frequently encountered across diverse medical applications, i.e., drug development, risk profiling, and clinical trials. We propose a theoretically grounded unified framework for counterfactual inference applicable to survival outcomes.
  arXiv  Detail & Related papers  (2020-06-14T01:15:00Z)
• Nonparametric Score Estimators [49.42469547970041]
  Estimating the score from a set of samples generated by an unknown distribution is a fundamental task in inference and learning of probabilistic models. We provide a unifying view of these estimators under the framework of regularized nonparametric regression. We propose score estimators based on iterative regularization that enjoy computational benefits from curl-free kernels and fast convergence.
  arXiv  Detail & Related papers  (2020-05-20T15:01:03Z)
• Estimating Treatment Effects with Observed Confounders and Mediators [25.338901482522648]
  Given a causal graph, the do-calculus can express treatment effects as functionals of the observational joint distribution that can be estimated empirically. Sometimes the do-calculus identifies multiple valid formulae, prompting us to compare the statistical properties of the corresponding estimators. In this paper, we investigate the over-identified scenario where both confounders and mediators are observed, rendering both estimators valid.
  arXiv  Detail & Related papers  (2020-03-26T15:50:25Z)
• The statistical physics of discovering exogenous and endogenous factors in a chain of events [0.0]
  We develop a method for estimating such extrinsic and intrinsic factors from a single series of event-occurrence times. The analysis is performed using a model that combines the inhomogeneous Poisson process and the Hawkes process.
  arXiv  Detail & Related papers  (2020-03-02T04:55:41Z)
• Orthogonal Statistical Learning [49.55515683387805]
  We provide non-asymptotic excess risk guarantees for statistical learning in a setting where the population risk depends on an unknown nuisance parameter. We show that if the population risk satisfies a condition called Neymanity, the impact of the nuisance estimation error on the excess risk bound achieved by the meta-algorithm is of second order.
  arXiv  Detail & Related papers  (2019-01-25T02:21:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.