no code implementations • 25 Jul 2023 • Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara
We consider estimation of parameters defined as linear functionals of solutions to linear inverse problems.
no code implementations • 10 Feb 2023 • Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara
In this paper, we study nonparametric estimation of instrumental variable (IV) regressions.
no code implementations • 17 Aug 2022 • Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara
In a variety of applications, including nonparametric instrumental variable (NPIV) analysis, proximal causal inference under unmeasured confounding, and 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 function (e. g., NPIV regression) defined by conditional moment restrictions.
no code implementations • 25 Mar 2022 • Victor Chernozhukov, Whitney Newey, Rahul Singh, Vasilis Syrgkanis
We extend the idea of automated debiased machine learning to the dynamic treatment regime and more generally to nested functionals.
1 code implementation • 26 Dec 2021 • Victor Chernozhukov, Carlos Cinelli, Whitney Newey, Amit Sharma, Vasilis Syrgkanis
Therefore, simple plausibility judgments on the maximum explanatory power of omitted variables (in explaining treatment and outcome variation) are sufficient to place overall bounds on the size of the bias.
no code implementations • 30 Dec 2020 • Victor Chernozhukov, Whitney Newey, Rahul Singh, Vasilis Syrgkanis
Many causal parameters are linear functionals of an underlying regression.
no code implementations • 24 Aug 2019 • Victor Chernozhukov, Whitney Newey, Vira Semenova
Second, we give a correction term for the transition density of the state variable.
no code implementations • 23 Feb 2018 • Victor Chernozhukov, Whitney Newey, Rahul Singh
To achieve this property, we include the Riesz representer for the functional as an additional nuisance parameter.
no code implementations • 30 Jan 2017 • Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey
A more general discussion and references to the existing literature are available in Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, and Newey (2016).
4 code implementations • 30 Jul 2016 • Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, James Robins
Fortunately, this regularization bias can be removed by solving auxiliary prediction problems via ML tools.