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Found 10 papers, 2 papers with code
Source Condition Double Robust Inference on Functionals of Inverse Problems
We consider estimation of parameters defined as linear functionals of solutions to linear inverse problems.
Minimax Instrumental Variable Regression and $L_2$ Convergence Guarantees without Identification or Closedness
In this paper, we study nonparametric estimation of instrumental variable (IV) regressions.
Inference on Strongly Identified Functionals of Weakly Identified Functions
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.
Automatic Debiased Machine Learning for Dynamic Treatment Effects and General Nested Functionals
We extend the idea of automated debiased machine learning to the dynamic treatment regime and more generally to nested functionals.
Long Story Short: Omitted Variable Bias in Causal Machine Learning
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.
Adversarial Estimation of Riesz Representers
Furthermore, we use critical radius theory -- in place of Donsker theory -- to prove asymptotic normality without sample splitting, uncovering a ``complexity-rate robustness'' condition.
Inference on weighted average value function in high-dimensional state space
Second, we give a correction term for the transition density of the state variable.
De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers
To achieve this property, we include the Riesz representer for the functional as an additional nuisance parameter.
Double/Debiased/Neyman Machine Learning of Treatment Effects
A more general discussion and references to the existing literature are available in Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, and Newey (2016).
Double/Debiased Machine Learning for Treatment and Causal Parameters
Fortunately, this regularization bias can be removed by solving auxiliary prediction problems via ML tools.
