mailitics

Tag: identification

  • Bounds and Identification of Joint Probabilities of Potential Outcomes and Observed Variables under Monotonicity Assumptions

    Bounds and Identification of Joint Probabilities of Potential Outcomes and Observed Variables under Monotonicity Assumptions arXiv:2602.18762v1 Announce Type: new Abstract: Evaluating joint probabilities of potential outcomes and observed variables, and their linear combinations, is a fundamental challenge in causal inference. This paper addresses the bounding and identification of these probabilities in settings with discrete treatment…

  • Partial Identification under Missing Data Using Weak Shadow Variables from Pretrained Models

    Partial Identification under Missing Data Using Weak Shadow Variables from Pretrained Models arXiv:2602.16061v1 Announce Type: new Abstract: Estimating population quantities such as mean outcomes from user feedback is fundamental to platform evaluation and social science, yet feedback is often missing not at random (MNAR): users with stronger opinions are more likely to respond, so standard…

  • Constrained Pareto Set Identification with Bandit Feedback

    Constrained Pareto Set Identification with Bandit Feedback arXiv:2506.08127v1 Announce Type: new Abstract: In this paper, we address the problem of identifying the Pareto Set under feasibility constraints in a multivariate bandit setting. Specifically, given a $K$-armed bandit with unknown means $mu_1, dots, mu_K in mathbb{R}^d$, the goal is to identify the set of arms whose…

  • Bayesian Optimization for Robust Identification of Ornstein-Uhlenbeck Model

    Bayesian Optimization for Robust Identification of Ornstein-Uhlenbeck Model arXiv:2503.06381v1 Announce Type: new Abstract: This paper deals with the identification of the stochastic Ornstein-Uhlenbeck (OU) process error model, which is characterized by an inverse time constant, and the unknown variances of the process and observation noises. Although the availability of the explicit expression of the log-likelihood…

  • dynoGP: Deep Gaussian Processes for dynamic system identification

    dynoGP: Deep Gaussian Processes for dynamic system identification arXiv:2502.05620v1 Announce Type: new Abstract: In this work, we present a novel approach to system identification for dynamical systems, based on a specific class of Deep Gaussian Processes (Deep GPs). These models are constructed by interconnecting linear dynamic GPs (equivalent to stochastic linear time-invariant dynamical systems) and…