3期

最新文章

• Professor Garib Nath Singh's contribution to the Discussion of 'Augmented balancing weights as linear regression' by Bruns-Smith et al. AI翻译
  作者：Singh, Garib Nath
  作者单位：Indian Institute of Technology System (IIT System); Indian Institute of Technology (Indian School of Mines) Dhanbad
• Maozai Tian, Shuo Liu, and Tan Meng's contribution to the Discussion of 'Augmented balancing weights as linear regression' by Bruns-Smith et al. AI翻译
  作者：Tian, Maozai; Liu, Shuo; Meng, Tan
  作者单位：Renmin University of China; Xinjiang University of Finance & Economics; Changji University
• Root cause discovery via permutations and Cholesky decomposition AI翻译
  作者：Li, Jinzhou; Chu, Benjamin B.; Scheller, Ines F.; Gagneur, Julien; Maathuis, Marloes H.
  作者单位：National University of Singapore; Stanford University; Technical University of Munich; Helmholtz Association; Helmholtz-Center Munich - German Research Center for Environmental Health; Technical University of Munich; Technical University of Munich; Swiss Federal Institutes of Technology Domain; ETH Zurich
  摘要：This work is motivated by the following problem: Can we identify the disease-causing gene in a patient affected by a monogenic disorder? This problem is an instance of root cause discovery. Specifically, we aim to identify the intervened variable in one interventional sample using a set of observational samples as reference. We consider a linear structural equation model where the causal ordering is unknown. We begin by examining a simple method that uses squared z-scores and characterize the ...
• Proposer of the vote of thanks to Bruns-Smith et al. and contribution to the Discussion of 'Augmented balancing weights as linear regression' AI翻译
  作者：Liu, Lin
  作者单位：Shanghai Jiao Tong University
• Inference on strongly identified functionals of weakly identified functions AI翻译
  作者：Bennett, Andrew; Kallus, Nathan; Mao, Xiaojie; Newey, Whitney K.; Syrgkanis, Vasilis; Uehara, Masatoshi
  作者单位：Morgan Stanley; Cornell University; Netflix, Inc.; Tsinghua University; Tsinghua University; Massachusetts Institute of Technology (MIT); Stanford University; Chan Zuckerberg Initiative (CZI)
  摘要：In a variety of applications, including nonparametric instrumental variable (NPIV) analysis, proximal causal inference under unmeasured confounding, and analysis of missing-not-at-random data with shadow variables, we are interested in inference on a continuous linear functional (e.g. average causal effects) of nuisance functions (e.g. NPIV regression) defined by conditional moment restrictions. These nuisance functions are often weakly identified, meaning the moment restrictions are ill-posed...
• Augmented balancing weights as linear regression AI翻译
  作者：Bruns-Smith, David; Dukes, Oliver; Feller, Avi; Ogburn, Elizabeth L.
  作者单位：Stanford University; Ghent University; University of California System; University of California Berkeley; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health
  摘要：We provide a novel characterization of augmented balancing weights, also known as automatic debiased machine learning. These popular doubly robust estimators combine outcome modelling with balancing weights-weights that achieve covariate balance directly instead of estimating and inverting the propensity score. When the outcome and weighting models are both linear in some (possibly infinite) basis, we show that the augmented estimator is equivalent to a single linear model with coefficients th...
• Liyang Sun's contribution to the Discussion of 'Augmented balancing weights as linear regression' by Bruns-Smith et al. AI翻译
  作者：Sun, Liyang
  作者单位：University of London; University College London
• Supriya Tiwari and Pallavi Basu's contribution to the Discussion of 'Augmented balancing weights as linear regression' by Bruns-Smith et al. AI翻译
  作者：Tiwari, Supriya; Basu, Pallavi
  作者单位：Indian School of Business (ISB)
  摘要：We provide a novel characterization of augmented balancing weights, also known as automatic debiased machine learning. These popular doubly robust estimators combine outcome modelling with balancing weights-weights that achieve covariate balance directly instead of estimating and inverting the propensity score. When the outcome and weighting models are both linear in some (possibly infinite) basis, we show that the augmented estimator is equivalent to a single linear model with coefficients th...
• Self-organizing state-space models with artificial dynamics AI翻译
  作者：Chen, Yuan; Gerber, Mathieu; Andrieu, Christophe; Douc, Randal
  作者单位：University of Bristol; IMT - Institut Mines-Telecom; Institut Polytechnique de Paris; Telecom SudParis
  摘要：We consider the problem of performing parameter and state inference in a state-space model (SSM) parametrized by a static parameter theta. A popular idea to address this problem consists of incorporating theta in the state of the system and allowing its time evolution, modelled as a Markov chain (theta t)t >= 1. This proxy model defines a so-called self-organizing SSM (SO-SSM) to which one may apply standard particle filters. However, the practical implementation of this idea in a theoreticall...
• Augmented balancing weights as linear regression AI翻译
  作者：Rotnitzky, Andrea; Smucler, Ezequiel; Robins, James
  作者单位：University of Washington; University of Washington Seattle; Harvard University; Harvard T.H. Chan School of Public Health
  摘要：We provide a novel characterization of augmented balancing weights, also known as automatic debiased machine learning. These popular doubly robust estimators combine outcome modelling with balancing weights-weights that achieve covariate balance directly instead of estimating and inverting the propensity score. When the outcome and weighting models are both linear in some (possibly infinite) basis, we show that the augmented estimator is equivalent to a single linear model with coefficients th...