arXiv subjects

Chase Mathis

Publications and source records attributed to Chase Mathis.

2 recordsLinked to original sources

arXiv2026

Confidence Horizons

Anytime-valid inference enables analysts to continuously monitor their data and stop experiments early. However, the majority of these methods incur a certain conservativeness by remaining valid on infinite time horizons. In practice, a bound on the horizon may be imposed due to budgetary, practical, or ethical constraints. In this paper, we ask the question: "Is it possible to obtain sharper large-sample anytime-valid inference by forgoing validity beyond some finite time horizon?". We provide a positive answer to this question by proposing a family of statistical objects that we call "confidence horizons". These objects can be viewed as large-sample confidence sequences on bounded time horizons, or alternatively as group sequential repeated confidence intervals with a maximal number of interim peeking times. We make explicit connections to the group sequential boundaries of Pocock [1977], O'Brien--Fleming [1979], and Wang--Tsiatis [1987]. We derive closed-form distribution functions of certain statistics which can be used to calculate the asymptotic quantiles of confidence horizons exactly, sidestepping the repeated integration typically employed in group sequential methods. We illustrate the use of confidence horizons for treatment effect estimation in sequentially randomized experiments under adaptive Neyman allocation.

stat.ME↗

arXiv2025

Exact Simulation of Longitudinal Data from Marginal Structural Models

Simulating longitudinal data from specified marginal structural models is a crucial but challenging task for evaluating causal inference methods and informing study design. While data generation typically proceeds in a fully conditional manner using structural equations according to a temporal ordering, it is difficult to ensure alignment between conditional distributions and the target marginal causal effects, which presents a fundamental challenge. To address this, we propose a flexible and efficient algorithm for simulating longitudinal data that adheres exactly to a specified marginal structural model. Our approach accommodates time-to-event outcomes and extends naturally to survival settings, which are prevalent in applied research. Compared to existing approaches, it offers several advantages: it enables exact simulation from a known causal model rather than relying on approximations; avoids restrictive assumptions about the data-generating process; and remains computationally efficient by requiring only the evaluation of analytical expressions, rather than Monte Carlo methods or numerical integration. Through simulation studies replicating realistic scenarios, we validate the method's accuracy and utility. Our method will facilitate researchers in effectively simulating data with target causal structures for their specific scenarios.

stat.ME↗