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
We develop a general theory of omitted variable bias for a wide range of common causal parameters, including (but not limited to) averages of potential outcomes, average treatment effects, average causal derivatives, and policy effects from covariate shifts.
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.