Zobrazeno 1 - 10
of 220
pro vyhledávání: '"Ogburn Elizabeth L."'
Autor:
Rosenblum, Michael, Chin, Elizabeth T., Ogburn, Elizabeth L., Nishimura, Akihiko, Westreich, Daniel, Datta, Abhirup, Vanderplas, Susan, Cuellar, Maria, Thompson, William C.
Since the National Academy of Sciences released their report outlining paths for improving reliability, standards, and policies in the forensic sciences NAS (2009), there has been heightened interest in evaluating and improving the scientific validit
Externí odkaz:
http://arxiv.org/abs/2309.04409
This paper addresses the asymptotic performance of popular spatial regression estimators of the linear effect of an exposure on an outcome under ``spatial confounding" -- the presence of an unmeasured spatially-structured variable influencing both th
Externí odkaz:
http://arxiv.org/abs/2308.12181
We provide a novel characterization of augmented balancing weights, also known as automatic debiased machine learning (AutoDML). These popular doubly robust or de-biased machine learning estimators combine outcome modeling with balancing weights - we
Externí odkaz:
http://arxiv.org/abs/2304.14545
Despite the growing interest in causal and statistical inference for settings with data dependence, few methods currently exist to account for missing data in dependent data settings; most classical missing data methods in statistics and causal infer
Externí odkaz:
http://arxiv.org/abs/2304.01953
An important strategy for identifying principal causal effects, which are often used in settings with noncompliance, is to invoke the principal ignorability (PI) assumption. As PI is untestable, it is important to gauge how sensitive effect estimates
Externí odkaz:
http://arxiv.org/abs/2303.05032
Autor:
De Silva, Ashwin, Ramesh, Rahul, Ungar, Lyle, Shuler, Marshall Hussain, Cowan, Noah J., Platt, Michael, Li, Chen, Isik, Leyla, Roh, Seung-Eon, Charles, Adam, Venkataraman, Archana, Caffo, Brian, How, Javier J., Kebschull, Justus M, Krakauer, John W., Bichuch, Maxim, Kinfu, Kaleab Alemayehu, Yezerets, Eva, Jayaraman, Dinesh, Shin, Jong M., Villar, Soledad, Phillips, Ian, Priebe, Carey E., Hartung, Thomas, Miller, Michael I., Dey, Jayanta, Ningyuan, Huang, Eaton, Eric, Etienne-Cummings, Ralph, Ogburn, Elizabeth L., Burns, Randal, Osuagwu, Onyema, Mensh, Brett, Muotri, Alysson R., Brown, Julia, White, Chris, Yang, Weiwei, Rusu, Andrei A., Verstynen, Timothy, Kording, Konrad P., Chaudhari, Pratik, Vogelstein, Joshua T.
Learning is a process which can update decision rules, based on past experience, such that future performance improves. Traditionally, machine learning is often evaluated under the assumption that the future will be identical to the past in distribut
Externí odkaz:
http://arxiv.org/abs/2201.07372
Over the past few decades, addressing "spatial confounding" has become a major topic in spatial statistics. However, the literature has provided conflicting definitions, and many proposed solutions are tied to specific analysis models and do not addr
Externí odkaz:
http://arxiv.org/abs/2112.14946
Autor:
Nguyen, Trang Quynh, Ogburn, Elizabeth L., Schmid, Ian, Sarker, Elizabeth B., Greifer, Noah, Koning, Ina M., Stuart, Elizabeth A.
Publikováno v:
Statistics Surveys. 2023. 17:1-41
This paper aims to provide practitioners of causal mediation analysis with a better understanding of estimation options. We take as inputs two familiar strategies (weighting and model-based prediction) and a simple way of combining them (weighted mod
Externí odkaz:
http://arxiv.org/abs/2102.06048
Publikováno v:
Journal of Causal Inference. 2022. 10:246-279
Causal mediation analysis is complicated with multiple effect definitions that require different sets of assumptions for identification. This paper provides a systematic explanation of such assumptions. We define five potential outcome types whose me
Externí odkaz:
http://arxiv.org/abs/2011.09537
Identification of treatment effects in the presence of unmeasured confounding is a persistent problem in the social, biological, and medical sciences. The problem of unmeasured confounding in settings with multiple treatments is most common in statis
Externí odkaz:
http://arxiv.org/abs/2011.04504