Zobrazeno 1 - 10
of 322
pro vyhledávání: '"Kallus Nathan"'
Autor:
Kallus Nathan, Santacatterina Michele
Publikováno v:
Journal of Causal Inference, Vol 10, Iss 1, Pp 123-140 (2022)
In causal inference, a variety of causal effect estimands have been studied, including the sample, uncensored, target, conditional, optimal subpopulation, and optimal weighted average treatment effects. Ad hoc methods have been developed for each est
Externí odkaz:
https://doaj.org/article/899d97d9d0df482abd9d770f2809d804
Autor:
Kallus Nathan, Santacatterina Michele
Publikováno v:
Journal of Causal Inference, Vol 9, Iss 1, Pp 345-369 (2021)
Marginal structural models (MSMs) can be used to estimate the causal effect of a potentially time-varying treatment in the presence of time-dependent confounding via weighted regression. The standard approach of using inverse probability of treatment
Externí odkaz:
https://doaj.org/article/465cea9f35504176bb3c9ec023c5a7cf
Conformal Prediction methods have finite-sample distribution-free marginal coverage guarantees. However, they generally do not offer conditional coverage guarantees, which can be important for high-stakes decisions. In this paper, we propose a novel
Externí odkaz:
http://arxiv.org/abs/2409.17466
This paper illustrates the central role of loss functions in data-driven decision making, providing a comprehensive survey on their influence in cost-sensitive classification (CSC) and reinforcement learning (RL). We demonstrate how different regress
Externí odkaz:
http://arxiv.org/abs/2409.12799
Autor:
Cho, Brian M, Pop, Ana-Roxana, Gan, Kyra, Corbett-Davies, Sam, Nir, Israel, Evnine, Ariel, Kallus, Nathan
When modifying existing policies in high-risk settings, it is often necessary to ensure with high certainty that the newly proposed policy improves upon a baseline, such as the status quo. In this work, we consider the problem of safe policy improvem
Externí odkaz:
http://arxiv.org/abs/2408.12004
Jackknife instrumental variable estimation (JIVE) is a classic method to leverage many weak instrumental variables (IVs) to estimate linear structural models, overcoming the bias of standard methods like two-stage least squares. In this paper, we ext
Externí odkaz:
http://arxiv.org/abs/2406.14140
Autor:
Oprescu, Miruna, Kallus, Nathan
Accurately predicting conditional average treatment effects (CATEs) is crucial in personalized medicine and digital platform analytics. Since often the treatments of interest cannot be directly randomized, observational data is leveraged to learn CAT
Externí odkaz:
http://arxiv.org/abs/2406.06452
Contextual linear optimization (CLO) uses predictive observations to reduce uncertainty in random cost coefficients and thereby improve average-cost performance. An example is a stochastic shortest path with random edge costs (e.g., traffic) and pred
Externí odkaz:
http://arxiv.org/abs/2405.16564
Autor:
He, Zhankui, Xie, Zhouhang, Steck, Harald, Liang, Dawen, Jha, Rahul, Kallus, Nathan, McAuley, Julian
Large language models (LLMs) are revolutionizing conversational recommender systems by adeptly indexing item content, understanding complex conversational contexts, and generating relevant item titles. However, controlling the distribution of recomme
Externí odkaz:
http://arxiv.org/abs/2405.12119
Autor:
Bibaut, Aurélien, Kallus, Nathan
Adaptive experiments such as multi-arm bandits adapt the treatment-allocation policy and/or the decision to stop the experiment to the data observed so far. This has the potential to improve outcomes for study participants within the experiment, to i
Externí odkaz:
http://arxiv.org/abs/2405.01281